NASA Report to the GSICS Executive Panel

James J. Butler NASA Goddard Space Flight Center

Greenbelt, MD USA

Japan Meteorological Agency Tokyo, Japan

July 15-16, 2013

1

Agenda

• Instrument Updates

– MODIS

– AIRS

– VIIRS

• Instrument Intercomparisons

• CLARREO Status

• Future Missions

2

Instrument Updates

3

MODIS Terra and Aqua Instrument and Data Processing Update

• Both Terra MODIS (13 years) and Aqua MODIS (11 years) and their on-board calibrators

continue to operate and function normally

• Only 1 additional noisy and inoperable detector in both Terra and Aqua MODIS over the

last 3 years

• Collection 6 L1B reprocessing completed and data released to public

– Atmos. and land reprocessing to be started in early May and July 2013

• Strong science applications using MODIS observations and data products

– Over 1000 new technical articles and 1500 new tech articles and proceedings

combined

Tech. and Proc. Articles: 8865 Avg. citation: 11.2/article Decade long high quality MODIS data

Products have significantly contributed To a broad range of scientific studies and

applications

Thru 3/2013

4

MODIS Level 1B Collection 6 Algorithm

• MODIS L1B Collection 6 (C6)

– Plan and development started as early as Jan, 2008

– Development, including all the changes to algorithms and

LUTs, completed (reviewed and approved) Feb, 2012

– List of algorithm changes provided in backup charts to

this presentation

– C6 data processing started Feb, 2012 for Aqua and Aug,

2012 for Terra

– Products released to public July, 2012 for Aqua and Nov,

2012 for Terra

5

AIRS Instrument and Data Processing Status

• AIRS is in excellent health

• AIRS version 6 Level 2 science product generation software has been released

– AIRS Level 1B products: calibrated, geolocated radiances

– AIRS Level 2 products: standard T, moisture, surface, ozone, cloud and outgoing longwave radiation, CO, CH4, SO2 and dust, cloud cleared radiance, and support product (i.e. higher resolution standard product profiles, trace gas abundances, detailed QA assessments)

– AIRS Level 3 products: global products in 1x1 degree grid bins in 3 temporal resolutions: daily, 8-day, and monthly

• Changes from version 5 were confined to Level 2 and Level 3—there were no changes to the AIRS Level 1B software (radiances)

• Reprocessing of Level 2 for the entire mission is in progress

6

Suomi VIIRS Instrument and Data Processing Status

• The Suomi VIIRS instrument and its on-board calibrators continue to operate and function normally

• The VIIRS Sensor Data Record (SDR) algorithm for the reflected solar bands now accounts for time and wavelength dependent reflectance degradation of the rotating telescope assembly mirrors

Impact of up to 5%

DNB

Early in the mission, SNPP VIIRS exhibited large gain degradation in the near infrared and shortwave infrared vs time on-orbit due to WOx contamination incurred during fabrication of the telescope mirrors

•The physical degradation mechanism was determined thru Aerospace witness mirror sample testing •The model for telescope reflectance degradation was determined using on-orbit solar diffuser/lunar data.

M1: 412nm; M2: 445nm; M3: 488nm

M4: 555nm; I1: 640m; M5: 672nm; M6: 746nm; M7: 865nm; I2: 865nm

M8: 1240nm; M9: 1378nm; I3: 1610nm; M10: 1610nm; M11: 2250nm

Curves: SD calibration gain trending Points: Lunar calibration gain trending

7

Instrument Intercomparisons

8

• At upcoming conferences, NASA JPL will present results from comparisons of AIRS, IASI, and CrIS

• There is excellent agreement among the instruments under clear and SNO conditions, but statistically significant disagreements under cloudy conditions

• Below is a plot of AIRS and CrIS brightness temperatures at 1231 cm-1

at Dome C differenced with in situ surface temperature measurements

NASA JPL AIRS/IASI/CrIS Comparisons

9

NASA Langley MTSAT-1R and MTSAT-2 Inter-comparisons

Uniform radiance

scenes show

little difference

Clear areas

near bright clouds

show larger

differences

-300 -200 -100 0 100 200 300-6x10

-5

-4x10-5

-2x10-5

0

2x10-5

4x10-5

6x10-5

PSF

pixel position

peak=1.0

A

Elliptical_Gauss

Develop MTSAT-1 point spread

function (PSF) to subtract

contribution from area surrounding

pixel

MTSAT-1R &

MODIS Aqua

comparison

before PSF

correction

MTSAT-1R &

MODIS Aqua

comparison after

PSF correction

This is a GSICS success story, Arata Okuyama (JMA) provided the MTSAT-2 commissioning images, that made this PSF correction possible

MTSAT-2 image

Dec 21, 2010, 0:30 GMT MTSAT-1R minus MTSAT-2

Dec 21, 2010, 0:30 GMT

10

NASA Langley Meteosat-9 0.65µm gain comparison

using the MODIS Aqua 0.65µm band as reference

• MODIS Terra/Met-9 ray-match inter-

calibration (first inter-calibrate MODIS

Terra to MODIS Aqua using SNOs over

poles)

• Aqua-MODIS/Met-9 ray-match inter-

calibration

• Deep Convective Calibration using

MODIS Aqua/Met-9 DCC reference

radiance to predict MET-9 DCC radiance

• Libya-4 Daily Exo-atmospheric Radiance

Model (DERM) (DERM built using

reference GEO inter-calibrated with

MODIS Aqua, then use DERM to predict

target GEO)

• SCIAMACHY/Met-9 ray-match inter-

calibration (first inter-calibrate

SCIAMACHY to MODIS Aqua using

SNOs)

Mean gains are within 1%

11

U. of Wisconsin Suomi NPP CrIS intercomparisons with EOS AIRS, Metop IASI and Suomi NPP VIIRS

•CrIS/AIRS brightness T (BT) intercomparisons using Simultaneous Nadir Overpasses (SNOs) over a wide range of latitude and longitude

•CrIS/IASI BT intercomparisons using Simultaneous Nadir Overpasses (SNOs) over northern, high latitude, nadir views

Comparison of the log scale BT distributions (i.e. left 6 plots) leads to mean BT difference

distribution agreements of 0.12⁰ or better (i.e. right 6 plots)

Comparison of CrIS and IASI mean BTs from

northern SNOs from April 2012 to November

2012

Weighted mean CrIS/IASI BT differences and

uncertainties are less than a few tenths K

Similar results obtained using southern SNOs 12

U. of Wisconsin Suomi NPP CrIS intercomparisons with

EOS AIRS, Metop IASI and Suomi NPP VIIRS

•CrIS/VIIRS brightness T (BT) intercomparisons

Time series of daily mean BT differences between

VIIRS and CrIS from February 2012 to April 2013

for VIIRS bands at 4µm, 10.8µm, and 12µm

Discontinuities due to adjustment to VIIRS

blackbody T knowledge (March 2012) and planned

VIIRS blackbody warm up/cool down linearity tests

Since April 2012, the mean BT differences are less

than 0.1K and are very stable

Scan angle, scene T and orbit phase effects on

VIIRS and CrIS BTs are being studied to fully

understand instrument performance

13

Multi-Instrument Inter-Calibration (MIIC) Framework

(C. Currey (PI NASA LaRC), A. Bartle, C. Lukashin, D. Doelling, and C. Roithmayr)

1) The MIIC Framework is a collection of software which predicts near co-incident measurements with matched viewing geometries for instruments on separate spacecraft and efficiently acquires these data from remote data servers using OPeNDAP and server-side functions. - The MIIC predictors account for the sensor operation mode (e.g. cross-track)

2) New server-side functions will complement those now in place for format translation and subset selection to minimize the computation and network demands placed on instrument teams that perform multi-instrument inter-calibration in the distributed and heterogeneous context of the NASA Earth Science infrastructure. Implemented server-side functions: - Data equal-angle gridding - Spectral convolution - Spatial convolution

3) The current MIIC project demonstrates LEO-GEO and LEO-LEO inter-calibrations use cases: - MODIS/Aqua and GOES-13; - MODIS/Aqua and SCIAMACHY/Envisat.

4) Future plans: 1) deploy MIIC web-services at the LaRC ASDC is planned; 2) collaborate with NOAA NCDC to access data from CLASS using MIIC services; 3) extend features; 4) continue to collaborate with GSICS Research and Data Working Groups

14

CLARREO Status

15

CLARREO Status: Reflected Solar (RS) Instrument (320nm to 2300nm)

1) Objective: Enabling climate benchmark using the Climate Absolute Radiance and Refractivity Observatory (CLARREO) for reference inter-calibration of existing operational sensors

-climate-focused mission currently in Pre-Phase A with mission and science definition teams working to advance the science of CLARREO, explore alternative implementation strategies, and reduce technical risk

2) CLARREO Reference Inter-calibration will provide data to determine and correct operational sensors for:

- Sensor offset and gain - Spectral response change on orbit - Sensitivity to Polarization - Non-linearity

3) CLARREO RS inter-calibration goal: uncertainty contribution ≤ 0.15% (k=1) over autocorrelation time period ≤ 0.8 year Wielicki & CLARREO SDT, “Achieving Climate Change Absolute Accuracy in Orbit”, BAMS 2013 Available online at http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-12-00149.1 4) High priority inter-calibration targets: - On-orbit sensors: CERES & VIIRS/JPSS, AVHRR/Metop, GEO Imagers, TEMPO, Landsats, ESA Sentinels (optical) - Surface: Dome C, desert sites - Space: lunar irradiance

16

CLARREO RS Instrument On-Orbit Pointing Operations

2-D pointing on-orbit is required. Time/space/angle matching to obtain ensemble of samples with data matching noise ≤ 1%

Figure: CLARREO RSS boresight locations, which matched JPSS cross-track data over one year time period. CLARREO in P90 orbit.

Matching requirements: - Within +/- 5 min of the JPSS passing; - VZA match within 1.4°; - RAZ match within 0.5o; - SZA < 75o;

- At least 10 km effective width of swath.

Inter-calibration sampling studies: - CLARREO in polar 90o inclination orbit - The ISS orbit - Sampling for both, LEO and GEO targets

17

CLARREO Reflected Solar (RS) Calibration Demonstration System (CDS)

Solar/Lunar for Absolute Reflectance Imaging Spectrometer (SOLARIS)

• Spectral range: 320 – 2300 nm • Spectral sampling: ≤4 nm • Spectral resolution: 8 nm • Swath width at nadir from 600 km orbit: >100 km • GIFOV < 0.5 × 0.5 km • Spatial resolution per sample:

– 70% of energy from within a 0.5 km x 0.5 km area – ≥ 95% within a 1.0 km x 1.0 km area

• SNR > 33 for λ < 900 nm • SNR > 25 for λ > 900 nm

• Polarization sensitivity for 100% polarized input: – <0.50% (TBD) below 1000 nm and – <0.75% (TBD) at other wavelengths

• Radiometric calibration accuracy: 0.3% of albedo (integration of reflectance across all wavelengths) and within individual bands

18

Full-field spectral and radiometric characterization

• Traceability flow from NIST to the CLARREO calibration

demonstration system (called SOLARIS) • POWR – Primary optical Watt Radiometer

CLARREO RS CDS (a.k.a. SOLARIS) Detector-based Calibration using Tunable Laser Source

On March 23, 2013, SOLARIS participated in a field intercalibration campaign at Red Lake Playa, Arizona, with ground based spectrometers and the NASA G-LiHT aircraft instrument coupled with Landsat-7 and Landsat-8 overflights

19

CLARREO InfraRed (IR) Calibration Demonstration System (CDS)

Compact, Demonstration, Four-Port, Fourier Transform Spectrometer operating from the mid to far-IR (5-50mm) with resolution 0.5cm-1

Goal is to measure brightness temperatures accurate to 0.1K (k=3), for 200 – 320K scenes (CLARREO IR Req.)

Bolometer

Detector

LN2

Cold Cal.

Source

Scene Select

Housing

Variable Scene

Temperature

Blackbody (VTBB)

Vac. Chamber

Housing CDS

Pyroelectric

Detector

(not visible)

•Characterize systematic radiance measurement uncertainty & refine instrument performance model •Develop a cost-effective instrument Design •Create a flexible and modular instrument design testbed operating in a controlled thermal and acoustic environment

20

2 hrs. data at each temperature Bolometer: 10pt., 5cm-1 bins Pyroelectric: 50pt., 25cm-1 bins

CLARREO IR CDS Brightness Temperature Results

21

Radiometric Accuracy Assessment w/ Current Design Complete: For the tested range of 200K to 320K scene temperature, over the spectral range 250-1350 cm-1 where responsivity for both detector channels is high:

• Radiance bias is generally less than 0.0015 W/m2 sr cm-1; • Brightness temperature bias is generally less than 0.2K; • Bolometer bias is dominated by uncorrected nonlinearity; • There may still be a source of bias in the pyroelectric channel that is not yet

accounted for. Full Measurement Uncertainty Report: July 24, 2013

CLARREO IR CDS Status

22

Future Missions

23

Upcoming Missions

Mission Instruments Projected Launch Date

TCTE TIM 10/2013

GPM Core GMI; DPR (Japan) 2/2014

ESSP/OCO-2 3 grating spectrometers 7/2014

SMAP L-band radiometer & radar 10/2014

SAGE-III (ISS) SAGE-III 12/2014

GOES-R ABI, GLM 10/2015

GRACE-FO (US/Germany) GPS; HAIRS; USO; SCA (Denmark); SSA (France) 8/2016

PFF-1 TSIS 8/2016

ICESat-2 ATLAS; GPS 12/2016

CYGNSS (EV-2) 8 GPS µ-satellites 2016-2017

JPSS-1 ATMS; CERES; CrIS; OMPS-N; VIIRS 2017

TEMPO (EV-1) UV & Vis Offner grating spectrometer 2017

ESSP/OCO-3 3 grating spectrometers 2017

SWOT Ka band radar interferometer 2020

PACE Ocean color spectrometer; polarimeter (TBD) 2020

JPSS-2 ATMS; RBI; CrIS; OMPS-N & L; VIIRS 2021

ASCENDS LIDAR >2021

ACE Spectrometer; polarimeter; LIDAR; Cloud Radar >2021

GEO-CAPE UV-Vis-NIR –IR imagers >2021

HyspIRI Hyperspectral & TIR imagers >2021

L-band SAR InSAR >2021

CLARREO IR &/or VisNiRSwir spectrometers; GNSS-RO 2022

Spacecraft provided by NASA Spacecraft not provided by NASA 24

Acknowledgements

• The material presented in this talk was provided by the colleagues listed below – MODIS: Jack Xiong (NASA) and the MODIS Characterization

Support Team

– AIRS & AIRS/IASI/CrIS comparisons: Denis Elliott (JPL)

– VIIRS: Jack Xiong (NASA) and the VIIRS Characterization Support Team

– MTSAT-1R and MTSAT-2 comparisons & Meteosat-9 0.65µm gain comparisons: Dave Doelling (NASA)

– CrIS/AIRS/IASI/VIIRS comparisons: Dave Tobin (U. of Wisconsin)

– Multi-Instrument Inter-Calibration Framework & CLARREO RSB: Costy Lukashin (NASA)

– CLARREO RSB CDS: Joel McCorkel (NASA)

– CLARREO IR CDS: Marty Mlynczak (NASA), Dave Johnson (NASA), & Rich Cageao (NASA)

25

Questions?

NASA Earth Science Division Operating Missions

26

Backup Slides

27

MODIS L1B C6 Product Changes

• Reflective Solar Bands (RSB)

– SD degradation at 936 nm included (previous degradation normalized at 936 nm)

– Time dependent RVS for all VIS/NIR bands, including bands 13-16

– Detector bias corrections (derived from EV data) and detector dependent RVS applied to Terra bands 3, 8-12 and Aqua bands 8-12

– EV response trending used to correct calibration drifts noticeable in recent years at different AOIs (including SD AOI) for Terra bands 1-4, 8, 9, 10 (proposed) and Aqua 8-9

• SD to provide radiometric calibration reference

• Lunar trending to track on-orbit radiometric change

• EV trending at different AOIs to track on-orbit changes in RVS

• Thermal Emissive Bands (TEB)

– Use BB cool-down data to compute TEB nonlinear calibration coefficients

– Use a0=0 for Terra PV bands mirror side 1 (mirror side 2 a0 is adjusted to minimize the mirror side difference) and a0=0 for Terra/Aqua b31-32

– Aqua pre-launch a2 (used in L1B) are adjusted to capture on-orbit changes using on-board BB calibration, while keeping the small initial difference

– Add FPA temperature dependence to the “fixed” b1 for Aqua bands 33, 35, and 36 when the BB is operated above their saturation temperatures

• Others

– Fill-value for inoperable detectors and QA flag for inoperable or noisy detectors at sub-frame level

– Improved implementation of calibration uncertainty algorithm (based on actual on-orbit calibration/retrieval with time, AOI, and scene dependence)

– L1B code fix for sector rotation data anomaly (during lunar roll) 28

• Journal Papers

– Xiong et. al, “Multi-year On-orbit Calibration and Performance of Terra MODIS Reflective Solar

Bands,” IEEE TGRS, Vol. 45, No. 4, 879-889, 2007

– Xiong et. al, “Multiyear On-orbit Calibration and Performance of Terra MODIS Thermal Emissive

Bands,” IEEE TGRS, 46 (6), 1790-1803, 2008

– Xiong et. al “Aqua MODIS Thermal Emissive Bands On-orbit Calibration, Characterization, and

Performance,” IEEE TGRS, 47(3), 803-814, 2009

– Xiong et. al, “On-Orbit Calibration and Performance of Aqua MODIS Reflective Solar Bands,” IEEE

TGRS 48(1), 535-546, 2010

– Toller et. al, “Terra and Aqua Moderate-resolution Imaging Spectroradiometer Collection 6 Level

1B Algorithm,” to be published, J. Appl. Remote Sensing, 2013

• SPIE Papers

– Xiong et. al, “Terra and Aqua MODIS calibration algorithms and uncertainty analysis,” Proc. SPIE

5978, no. 59780V (2005)

– Sun et. al “MODIS RSB calibration improvements in Collection 6” Proc. SPIE 8528, no. 85280N

(2012)

– Wenny et. al “MODIS TEB calibration approach in collection 6” Proc. SPIE 8533, no. 85331M (2012)

MODIS References

29

Recent References on CLARREO Inter-calibration in the RS

C.M. Roithmayr, and P.W. Speth, Chap. 13, “Analysis of Opportunities for Intercalibration between Two Spacecraft," Advances in Engineering Research, Vol. 1, edited by V. M. Petrova, Nova Science Publishers, Hauppauge, NY, 2012, pp. 409 - 436.

C.M. Roithmayr, C. Lukashin, P.W. Speth, K. Thome, B.A. Wielicki, D.F. Young, “CLARREO Approach for On-Orbit Reference Inter-Calibration of Reflected Solar Radiance Sensors,” submitted to IEEE Tran. Geo. Rem. Sensing, February, 2013.

C.M. Roithmayr, C. Lukashin, P.W. Speth, K. Thome, D.F. Young, B.A. Wielicki, “Opportunities to Intercalibrate Radiometric Sensors from International Space Station,” in preparation for submission to JTECH, July, 2013.

C. Lukashin, B.A. Wielicki, D.F. Young, K. Thome, Z. Jin, and W. Sun, “Uncertainty Estimates for Imager Reference Inter-Calibration With CLARREO Reflected Solar Spectrometer”, IEEE TGRS, Special Issue on Instrument Inter-calibration, DOI: 10.1109/TGRS.2012.2233480, 2012.

Wenbo Sun and C. Lukashin, “Modeling polarized solar radiation from ocean-atmosphere system for CLARREO inter-calibration applications,” Atmos. Chem. Phys. Discuss., 13, 1–58, 2013.

C. Lukashin, Z. Jin, D.G. Macdonnell, K. Thome, W. Sun, B.A. Wielicki, D.F. Young , “Requirement for Instrument Sensitivity to Polarization for Climate Observing System in Reflected Solar,” in preparation for submission to Journal of Geophysical Research, 2013.

Wielicki & CLARREO SDT, “Achieving Climate Change Absolute Accuracy in Orbit”, BAMS 2013 Available online at http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-12-00149.1

30

Acronyms

ABI Advanced Baseline Imager

ACE Aerosol-Clouds-Ecosystems

ASCENDS Active Sensing of CO2 Emissions over Nights, Days, and Seasons

ATMS Advanced Technology Microwave Sounder

ATLAS Advanced Topographic Laser Altimeter System

CLARREO Climate Absolute Radiance and Refractivity Observatory

CERES Clouds and the Earth’s Radiant Energy System

CrIS Crosstrack Infrared Sounder

CYGNSS Cyclone Global Navigation Satellite System

DPR Dual frequency Precipitation Radar

ESSP Earth System Science Pathfinder

EV Earth Venture

GLM Geostationary Lightning Mapper

GEO-CAPE GEOstationary Coastal and Air Pollution Events

GMI GPM Microwave Imager

GNSS-RO Global Navigation Satellite Systems- Radio Occultation

GPM Global Precipitation Measurement

GPS Global Positioning System

GRACE-FO Gravity Recovery And Climate Experiment-Follow On

HyspIRI Hyperspectral InfraRed Imager

ICESat Ice, Cloud, and land Elevation Satellite

InSAR Interferometric Synthetic Aperture Radar

JPSS Joint Polar Satellite System

LIDAR LIght Detection And Ranging

OCO Orbiting Carbon Observatory

OMPS Ozone Mapping and Profiler Suite

PACE Pre-Aerosol, Clouds, and ocean Ecosystem

RBI Radiation Budget Instrument

SAGE Stratospheric Aerosol and Gas Experiment

SCA Star Camera Assembly

SIRCUS Spectral Irradiance and Radiance responsivity Calibrations using Uniform Sources

SMAP Soil Moisture Active Passive

SOLARIS Solar/Lunar for Absolute Reflectance Imaging Spectrometer

SSA SuperStar Accelerometer

SWOT Suface Water Ocean Topography

TCTE Total solar irradiance Calibration Transfer Experiment

TEMPO Tropospheric Emissions: Monitoring of Pollution

TIM Total Irradiance Monitor

TSIS Total Solar Irradiance Sensor

VIIRS Visible Infrared Imager Radiometer Suite

31