Global Space-Based Inter-Calibration System

(GSICS)

NOAA GPRC Report for EP-15

Presented by Mitch Goldberg NOAA GSICS EP Member

NOAA GSICS Team

Fuzhong Weng, Larry Flynn, Manik Bali, Chengzhi Zou, Likun Wang, Xiaolei Zou, Fangfang Yu, Changyong Cao, Fred Wu ,Jian Zeng

22

� Detail validation and assessment of SUOMI NPP for GSICS reference

instruments

� Use of SUOMI NPP instruments in current GSICS monitoring tools

Current Focus

33

Review Outcomes: SNPP SDR Products Review Meeting was held on Dec. 18-20, 2013. NESDIS Senior Management Leads: Ms. Mary Kicza and Dr. Al Powell attended the review. The Cal/Val team scientists presented the results on their specific calval tasks and NWP and other users NWS/NOS offered their independent assessments of data product quality based on their intensive cal/val analyses. The review panel recommended that the CrIS, ATMS and VIIRS SDR products be ready to be declared validated scientifically. And three remaining issues were recommended to resolve before OMPS EV SDR goes to the validated stage: cross-track effects in NM need to be addressed; Stray-light improvements still needed in NP SDR; Artificial separation between EV SDR and Cal SDR should be eliminated

Significance: Suomi NPP CrIS and ATMS SDR products are continuing NOAA afternoon orbits sounding data for NWS NWP radiance assimilation. It is shown from CEP global forecast system (GFS) and ECMWF global models that uses of CrIS and ATMS data have similar or slightly better impacts on the global medium-range forecasts

SNPP SDR Products Review for Declaring the Validated Maturity

Attendees for SUOMI NPP SDR Product Review Meeting in NOAA Center for Weather and Climate Prediction Auditorium

3

44

JPSS Science POCs and Leads at NOAA/NASA

ProgramMitch Goldberg – NOAA Program

ScientistJim Gleason – NASA Project Scientist

Flight ProjectJim Butler – Project Scientist

ATMSEd Kim – Instrument Scientist

CrISDave Johnson – Instrument Scientist

OMPSGlen Jaross – Instrument Scientist

VIIRSKurt Thome – Instrument Scientist

CERESKory Priestley – Instrument Scientist

Ground Segment -SDR

Fuzhong Weng – STAR SDR LeadBruce Guenther – DPA SDR Lead

Ground Segment - EDRIvan Csiszar , Ingrid Guch, Paul Digacomo–

STAR EDR LeadRay Godin– DPA EDR Lead

ATMS SDRFuzhong Weng – ATMS SDR Lead

CrIS SDRYong Han – CrIS SDR Lead

OMPS SDRXianqian Wu –OMPS SDR Lead

VIIRS SDRChangyong Cao – VIIRS SDR Lead

EDR AlgorithmsJeff Key – Cryosphere EDRsLarry Flynn – Ozone EDRsIvan Csiszar – Land EDRs

Alexander Ignatov – SST EDRsDon Hilger – Imagery EDRs

Tony Reale (acting) – Sounding EDRs

Andy Heidinger – Cloud EDRsIstvan Laszlo – Radiation Budget

EDRsMenghua Wang – Ocean Color EDRShobha Kondragunta – Aerosol EDRs

4

55 5

Suomi NPP TDR/SDR Algorithm Schedule

C

CCCCCCCCCCCCC

Sensor Beta Provisional ValidatedCrIS February 10, 2012 February 6, 2013 March 18, 2014

ATMS May 2, 2012 February 12, 2013 March 18, 2014

OMPS March 7, 2012 March 12, 2013 June, 2014VIIRS May 2, 2012 March 13, 2013 April 16, 2014

Beta• Early release product.• Initial calibration applied• Minimally validated and may still contain significant errors (rapid changes can be expected. Version changes will not be identified as errors are corrected as on-orbit baseline is not established)• Available to allow users to gain familiarity with data formats and parameters• Product is not appropriate as the basis for quantitative scientific publications studies and applicationsProvisional• Product quality may not be optimal• Incremental product improvements are still occurring as calibration parameters are adjusted with sensor on-orbit characterization (versions will be tracked)• General research community is encouraged to participate in the QA and validation of the product, but need to be aware that product validation and QA are ongoing• Users are urged to consult the SDR product status document prior to use of the data in publications• Ready for operational evaluationValidated• On-orbit sensor performance characterized and calibration parameters adjusted accordingly• Ready for use in applications and scientific publications• There may be later improved versions• There will be strong versioning with documentation

66

JGR Special Issue on Suomi NPP CalVal

34 papers have been accepted in AGU Journal Geophysical Research Special Issue on Suomi NPP satellite calibration, validation and applications.

Guest Editor: Fuzhong Weng

6

77

Suomi NPP Calibration/Validation Schedule

• Four Phases of Cal/Val:1. Pre-Launch; all time prior to launch – Algorithm verification, sensor testing, and validation preparation2. Early Orbit Check-out (first 30-90 days) – System Calibration & Characterization3. Intensive Cal/Val (ICV); extending to approximately 24 months post-launch – xDR Validation4. Long-Term Monitoring (LTM); through life of sensors after ICV

• For each phase:– Exit Criteria established– Activities summarized– Products mature through phases independently

LA

UN

CH

ICVEOC LTM

NPP Launch

Build Team

Resource ID& Development

Sensor Characterization

Post-LaunchPlan Dev.

Alg. Assessment& Verifications

Cal/Val ToolDevelopment

Sens or Charar.

&Calibration

Quick-Look Analysis

SDRs/EDRs

SDR/EDR Alg.Tuning

Estab. SensorStability

SDR Validation

Key EDR Validation

Mission Integration

Product Ops Viability

Monitor Sensor Stability

EDR Validation

PRE-LAUNCH

LA

UN

CH

ICVEOC LTM

NPP Launch

Build Team

Resource ID& Development

Sensor Characterization

Post-LaunchPlan Dev.

Alg. Assessment& Verifications

Cal/Val ToolDevelopment

Sens or Charar.

&Calibration

Quick-Look Analysis

SDRs/EDRs

SDR/EDR Alg.Tuning

Estab. SensorStability

SDR Validation

Key EDR Validation

Mission Integration

Product Ops Viability

Monitor Sensor Stability

EDR Validation

PRE-LAUNCH

10-28-2011 12-18-2013 Validated Review10-23-2012-Provisional Review

Joint Polar Satellite System

7

88

Time-Series of GEO- LEO ( GOES-15 - AIRS/CRIS/IASI Difference)

Figure: Time-series of GEO-LEO inter-calibration

between GOES and AIRS/CrIS/IASI trace each

other very well, indicating the long-term

radiometrical calibration stability of the three

hyperspectral radiometers

o GOES-15 Imager displays slight

seasonal variation at Ch4 and Ch6

o The mean Tb difference between

AIRS/CrIS/IASI is very small <0.1-0.2K

Key Results/Accomplishments

Fangfang Yu

Achieving 0.1 K absolute calibration is important for verifying real climate trends

• Through detail validation we have demonstrated that both CrIS and IASI have achieved a high level climate monitoring performance capability.

• Climate monitoring performance allows you to minimize the time to detect a real climate trend from natural variability.

• In the figure to the right, we see that a trend of 0.1 K per decade would take 20 years to confirm with perfect observations.

• While a calibration accuracy of 0.1 would take about 25-27 years

• While a calibration accuracy of 0.3 would take about 50 years.

• This chart would imply that CrIS and IASI are not good for monitoring trends. The accuracy noted in the chart for IASI, AIRS, CRIS are from the specification

• Good news - CrIS and IASI are approaching 0.1 K - beating the specification by significant margins 9

Wielicki, Bruce A., and Coauthors, 2013: Achieving Climate Change Absolute Accuracy in Orbit. Bull. Amer. Meteor. Soc., 94, 1519–1539.doi: http://dx.doi.org/10.1175/BAMS-D-12-00149.1

CriS Specifications (note the larger numbers)

10

(I do not have the actual IASI spec)

N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N

How do we know how accurate CriS and IASI are?

• We compare CrIS with airborne interferometer on

NASA’s ER-2. The airborne instrument are tied to SI

reference (NIST)

• We underfly the S-NPP satellite and compare

• We also compare IASI and CrIS and simultaneous

nadir overpass locations

11

12

JPSS PGRR Deep-Dive Validation First S-NPP ER-2 Aircraft Campaign to provide

validation for CrIS, ATMS and VIIRS

NIST traceable absolute calibration for CrIS

ER-2 with aircraft validation sensors under flies Suomi NPP sensors. In the case of CrIS,the validation sensor in this example is from the Scanning High-resolution InterferometerSounder (S-HIS) which has been tied to a NIST traceable calibration source.Quick look comparisons show excellent agreement. Significance – NIST traceable validation is criticalfor uncertainty analysis needed to fully assess data quality of S-NPP and JPSS sensors.

May 10, 2013 – first look

10/23-24/2012 NPP SDR Provisional Product Review 13

Atmospheric absorption above the aircraft

Window channels

CrIS versus IASI/MetOp-A

14

South Pole (1112)North Pole (987)

Bias: CrIS-IASI

STDEV: CrIS-IASI

Bias: CrIS-IASI

STDEV: CrIS-IASI

CrIS versus IASI/MetOp-B

15

South Pole (809)North Pole (774)

Bias: CrIS-IASI

STDEV: CrIS-IASI

Bias: CrIS-IASI

STDEV: CrIS-IASI

Next Steps

• Aircraft campaigns shows absolute accuracy between 0.1 – 0.2C

• SNOs over the arctic shows IASI and CrIS differences generally within 0.1 C

• However larger differences for Antarctic SNOs

– Water vapor band still within +- 0.1

– CO2 Longwave ~ 0.2 (MeTOP-B)

– Shortwave Infrared much larger (IASI high noise is this region).

• Next step - new campaigns over the Antarctic (or over cold extreme areas - Greenland) to

reconcile these difference

• CrIS and IASI are not just weather sensors. Results indicate high accuracy and stability.

Can be used as anchor points for NWP!

16

1717

BEFORE accounting for SRF difference AFTER accounting for SRF difference

Changyong Cao

Key Results/AccomplishmentsVIIRS and MODIR RSB InterVIIRS and MODIR RSB Inter--comparison at SNOcomparison at SNO--x ( over desert) x ( over desert)

1818

ATMS Post-launch Characterization of Calibration Accuracy through O-B

Bia

s (K

)

On-orbit ATMS calibration accuracy is characterized using GPSRO and ECMWF data as input to RT model and is better than specification for most of sounding channels.

O -

B (

K)

18

1919

SNPP ATMS Has Stable Noise

19 19

2020 20

Biases in the Tropics (NOAA-15, MetOp-A, SNPP)

before after

ATMS channel 10

ATMS channel 11

ATMS channel 13

ATMS channel 14

NOAA-18 is subtracted. The pentad data set within ±30o latitudinal band.

2121

� Deep Convective Cloud (DCC) is selected as

common reference target for visible instrument

inter-calibration

� Confirmed the seasonal and annual variation of

DCC reflectance

� Used long-term MODIS data to determine the

DCC reference reflectance for the area

o Calibration coefficient difference between

median DCC and desert method is less than

1%

� Demonstrated that less variations with median

values in the time-series of DCC reflectance

o The bias between the median and mode

reflectance is less than 0.4%

Key Results/AccomplishmentsCharacterization of Deep Convective Cloud as Calibration reference

Fangfang Yu

222222

� Reflectance of the lunar surface is extremely stable

� USGS ROLO lunar irradiance model make high accurate relatively calibration possible

� GOES-15 rolled northward to trace the Moon to characterize the scan mirror angular

dependent reflectivity during its PLT period

� Results showed that the EW oversampling factor is not constant for the GOES-15 Imager

visible channel

� Early result showed about 1.6% reflectance variation.

� Yu, F., X. Wu, T. Stone, and G. Sindic-Rancic, 2013b. Angular Variation of GOES Imager

scan-mirror visible reflectivity, GSICS Quarterly Newsletter, Vol. 7 (3), 9-10.

Fangfang Yu

Key Results/Accomplishments

Determination of Scan Mirror Angular Reflectivity using the Moon

2323

� NOAA Inter-Calibrated MSU/AMSU FCDR

products went through GSICS Review for

GSICS Procedure for Product

Acceptance (GPPA) on 12/03/2013

� After review, the products were approved

by GSICS Executive Panel to go to

GSICS Pre-Operational Phase

� A Link to NOAA NCDC product website

was provided in the GSICS products

catalog

� IMICA calibrated MSU/AMSU FCDR

products open to GSICS users

Figure: Overlapping orbital observations between

NOAA-15 and NOAA-18 during 07/01/2008. Inter-satellite

biases by the IMICA inter-calibration for randomly selected

geo-locations (the dotted square box) exhibited near zero

inter-satellite biases (the larger box pointed by the arrow)

Cheng-Zhi Zou

Key Results/Accomplishments

NOAA Calibrated MSU/AMSU FCDR went through GSICS Review

2424

OMPS Nadir Mapper(NM) versus GOME-2 METOp-A (left) and METOp-B (right) at SNOx

o The inter-calibration between OMPS and GOME-2 confirmed that the signals for GOME-2 on METOp-A have

been degradated for both the earthshine and solar measurements by up to 50% after more than seven years in

orbit. Since METOp-B was launched in September 2012, the comparison shows much better agreement.

o Also, the inter-calibration demonstrates that the GOME-2 diffuser has degraded more at shorter wavelengths

than at longer wavelengths, which leads to the current 10-15% discrepancy in reflectance for shorter

wavelengths.

Key Results/Accomplishments

0.7

0.8

0.9

1

1.1

1.2

2013-01 2013-03 2013-04 2013-08 2013-09 2014-01 2014-02

OM

PS

NM

Re

fl./

ME

TO

p-B

Re

fl.

Month

315nm 325nm 335nm

345nm 355nm 365nm0.7

0.8

0.9

1

1.1

1.2

OM

PS

NM

Re

fl./

ME

TO

p-A

Re

fl.

Month

315nm 325nm 335nm

345nm 355nm 365nm

Jian Zeng & Fred Wu

2525

OMPS Nadir Profiler(NP) versus GOME-2 METOp-A (left) and METOp-B (right)

Despite the large FOV difference, the reflectance discrepancy between OMPS NP and METOp-B

band 1B is within ~10%. For METOp-B band 1A, the discrepancy is a bit larger.

Key Results/Accomplishments

0.3

0.5

0.7

0.9

1.1

1.3

1.5

1.7

2013-012013-03 2013-042013-08 2013-092014-01 2014-02OMPS NP Refl./METOp-B Refl.

Month

259nm 269nm 279nm

289nm 299nm 309nm

0.3

0.5

0.7

0.9

1.1

1.3

1.5

1.7

OMPS NP Refl./METOp-A Refl.

Month

259nm 269nm 289nm

299nm 309nm

Jian Zeng & Fred Wu

2626

THANK YOU