Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005 Presented at MCST Calibration breakout meeting, March 22, 2005.

Slides:



Advertisements
Similar presentations
Evaluating Calibration of MODIS Thermal Emissive Bands Using Infrared Atmospheric Sounding Interferometer Measurements Yonghong Li a, Aisheng Wu a, Xiaoxiong.
Advertisements

Summary of Terra and Aqua MODIS Long-term Performance Jack Xiong 1, Brian Wenny 2, Sri Madhaven 2, Amit Angal 3, William Barnes 4, and Vincent Salomonson.
Aqua MODIS Cold FPA Performance and Operation MODIS Characterization Support Team (MCST) April 25, 2012.
MODIS Atmosphere Solar Reflectance Issues 1. Aqua VNIR focal plane empirical re-registration status Ralf Bennartz, Bob Holz, Steve Platnick 2 1 U. Wisconsin,
Presentation on OMPS Nadir Mapper Wavelength Shift Adjustment for Earth-view Measurements.
MCST – MODLAND Eric F. Vermote –MODLAND representative.
Scanner Characteristics
MTSAT-1R Band 5 Anomaly Apparent ghosting in CCD imager Warmer scenes introduce cold bias to adjacent block of pixels. Prepared by Chris Schmidt
MOD06 Cloud Top Properties Richard Frey Paul Menzel Bryan Baum University of Wisconsin - Madison.
Remote Sensing of Mesoscale Vortices in Hurricane Eyewalls Presented by: Chris Castellano Brian Cerruti Stephen Garbarino.
1 Calibration Adjustments for the MODIS Aqua 2015 Ocean Color Reprocessing Gerhard Meister, NASA Code 616 OBPG (Ocean Biology Processing Group) 5/18/2015.
MODIS Regional and Global Cloud Variability Brent C. Maddux 1,2 Steve Platnick 3, Steven A. Ackerman 1,2, Paul Menzel 1, Kathy Strabala 1, Richard Frey.
Cross Validation of Thermal Infrared Remotely Sensed Data In-Flight Using Automated Validation Sites © 2010 California Institute of Technology. Government.
Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison E. Eva Borbas, Zhenglong Li and W. Paul Menzel Cooperative.
NPP VIIRS M6 Saturation Study Update
Orbit Characteristics and View Angle Effects on the Global Cloud Field
High Spectral Resolution Infrared Land Surface Modeling & Retrieval for MURI 28 April 2004 MURI Workshop Madison, WI Bob Knuteson UW-Madison CIMSS.
NASA, CGMS-41, July 2013 Coordination Group for Meteorological Satellites - CGMS Calibration/validation of Operational Instruments at NASA Langley Research.
MODIS Geolocation Status Robert Wolfe, Mash Nishihama, Al Fleig, James Kuyper MODIS Science Team Meeting December 19, 2001.
An Estimate of Contrail Coverage over the Contiguous United States David Duda, Konstantin Khlopenkov, Thad Chee SSAI, Hampton, VA Patrick Minnis NASA LaRC,
Hank Revercomb, David C. Tobin, Robert O. Knuteson, Fred A. Best, Daniel D. LaPorte, Steven Dutcher, Scott D. Ellington, Mark W.Werner, Ralph G. Dedecker,
11 Ice Cover and Sea and Lake Ice Concentration with GOES-R ABI Presented by Yinghui Liu Presented by Yinghui Liu 1 Team Members: Yinghui Liu, Jeffrey.
University of Wisconsin - Madison Space Science and Engineering Center (SSEC) High Spectral Resolution IR Observing & Instruments Hank Revercomb (Part.
USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005.
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
MODIS Collection 6 MCST Proposed Changes to L1B. Page 2 Introduction MODIS Collection History –Collection 5 – Feb present –Collection 4 – Jan.
UW status/report: 1. Impact of new FIR filter 2. IDPS/ADL comparisons CrIS SDR Cal/Val Telecon 25-Apr-2012.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
Inter-calibration of Operational IR Sounders using CLARREO Bob Holz, Dave Tobin, Fred Nagle, Bob Knuteson, Fred Best, Hank Revercomb Space Science and.
Analysis of Nonlinearity Correction for CrIS SDR April 25, 2012 Chunming Wang NGAS Comparisons Between V32 and V33 Engineering Packets.
Radiative transfer in the thermal infrared and the surface source term
Initial Analysis of the Pixel-Level Uncertainties in Global MODIS Cloud Optical Thickness and Effective Particle Size Retrievals Steven Platnick 1, Robert.
MODIS Preprocessing (before L1B) Changes over the Last Year (and looking forward) Chris Moeller and others CIMSS; Univ. Wisconsin July 13, 2004 Thanks.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
CoRP Cal/Val Symposium July 13, 2005
Latest Terra-MODIS Ocean Color Radiance Corrections Inter-detector, Mirror Side, Cross-Scan, and Gain Adjustments Bob Evans, Ed Kearns, Kay Kilpatrick.
Data acquisition From satellites with the MODIS instrument.
Analysis of Simultaneous Nadir Observations of MODIS from AQUA and TERRA Andrew Heidinger and many others NOAA/NESDIS Office of Research and Applications.
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison South Africa, April 2006.
Interannual Variability and Decadal Change of Solar Reflectance Spectra Zhonghai Jin Costy Loukachine Bruce Wielicki (NASA Langley research Center / SSAI,
AIRS Land Surface Temperature and Emissivity Validation Bob Knuteson Hank Revercomb, Dave Tobin, Ken Vinson, Chia Lee University of Wisconsin-Madison Space.
Early MODIS Atmospheric Science Products: Radiances, Cloud Detection, Cloud Properties, and Atmospheric Profiles Steven A. Ackerman, Richard A. Frey, Liam.
AIRS – MODIS TEB Global Comparisons Chris Moeller Dave Tobin Univ. Wisconsin May 13, 2008 MODIS Calibration Mtg.
MODIS Infrared Atmospheric Profiles and Water Vapor: Updates for Collection 5 Suzanne Seemann, Eva Borbas, Jun Li, Liam Gumley Cooperative Institute for.
Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005.
Japan Meteorological Agency, June 2016 Coordination Group for Meteorological Satellites - CGMS Non-Meteorological Application for Himawari-8 Presented.
Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison Tiejun Chang and Xiangqian Wu GSICS Joint Research and data Working.
SST from MODIS AQUA and TERRA Kay Kilpatrick, Ed Kearns, Bob Evans, and Peter Minnett Rosenstiel School of Marine and Atmospheric Science University of.
Hyperspectral Cloud-Clearing Allen Huang, Jun Li, Chian-Yi Liu, Kevin Baggett, Li Guan, & Xuebao Wu SSEC/CIMSS, University of Wisconsin-Madison AIRS/AMSU.
Joint GRWG and GDWG Meeting February 2010, Toulouse, France
GSICS Web Meeting, 17 November 2011
Paper under review for JGR-Atmospheres …
Lunar observation data set preparation
NASA Aqua.
TOA Radiative Flux Estimation From CERES Angular Distribution Models
An Overview of MODIS Reflective Solar Bands Calibration and Performance Jack Xiong NASA / GSFC GRWG Web Meeting on Reference Instruments and Their Traceability.
Aqua MODIS Reflective Solar Bands (RSB)
AIRS (Atmospheric Infrared Sounder) Instrument Characteristics
On the use of Ray-Matching to transfer calibration
Fangfang Yu and Fred Wu 22 March 2011
Calibration and Performance MODIS Characterization Support Team (MCST)
MODIS Characterization and Support Team Presented By Truman Wilson
Using the Moon for Sensor Calibration Inter- comparisons
Manik Bali Jonathan Mittaz
Satellite Sensors – Historical Perspectives
AHI IR Tb bias variation diurnal & at low temperature
GOES -12 Imager April 4, 2002 GOES-12 Imager - pre-launch info - radiances - products Timothy J. Schmit et al.
Early calibration results of FY-4A/GIIRS during in-orbit testing
Use of GSICS to Improve Operational Radiometric Calibration
Presentation transcript:

Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005 Presented at MCST Calibration breakout meeting, March 22, 2005

Recent MODIS Calibration Changes and Issues Collect 5 DSM RVS application for Terra (MSCN?) Aqua AIRS – MODIS comparisons 5um leak correction for Terra Aside 2 TIR band destriping update (global training) Reminder about Aqua MODIS registration offsets (Ralf Bennartz) Terra B26 radiance change for Collect 5 due to change in 5um leak correction formulation

Terra MODIS DSM RVS Used for Thermal bands Largest impact in LWIR CO2 bands Cross-track asymmetry reduced Mirror-side striping reduced (exception: Bside) Examples from each of the major MODIS epochs (Aside 1, Bside, Aside 2)

Mirror side 2Mirror side 1 Band 36 across track profiles using old and DSM RVS DSM RVS improves across track symmetry, esp Mirror Side 2

Aside 1 BOS-EOS BOS EOS NADIR MSCN pattern 1 Granule average

Aside 1 BOS-EOS BOS EOS NADIR MSCN pattern 1 Granule average

Aside 1 Collect 4

Aside 1 Collect 5

Aside 1

BOS-EOS BOS EOS NADIR MSCN pattern 2 Aside 2 Granule average

BOS-EOS BOS EOS NADIR MSCN pattern 2 Aside 2 Granule average

Aside 2 Collect 4

Aside 2 Collect 5

Aside 2

Bside Collect 4

Bside Collect 5

Bside

Old RVS Terra MODIS Clear Sky Radiance Bias (MODIS – model prediction) Band 36 (14.2 um) The biases have a distinct dependence on scan angle

DSM RVS Using DSM RVS largely removes the bias dependence on scan angle Terra MODIS Clear Sky Radiance Bias (MODIS – model prediction) Band 36 (14.2 um)

Aqua AIRS-MODIS Comparisons Global day of data analyzed…uniform scenes only Suggests a MODIS calibration bias as function of BT. Suggests a MODIS Scan Mirror RVS error.

The 1 km MODIS is collocated with AIRS by representing the AIRS FOVs as slightly oversized circular footprints, and computing the mean MODIS value within those footprints for each band. Spatially uniform scenes are selected by requiring the standard deviation of the MODIS data within each AIRS footprint to be 0.2K or less.

Example comparisons for band 34 (13.7  m) on 6 Sept AIRS MODIS

Brightness temperature differences as a function of scene temperature.  m September February K Q: What causes scene temperature dependence of bias??

Band 35 (13.9  m) brightness temperature differences for one orbit of data on 6 Sept 2002 using the nominal MODIS SRF (black) and using the MODIS SRF shifted by +0.8 cm -1 (red). unshiftedshifted

Aqua MODIS Clear Sky Radiance Bias: Band 36 Before spectral shift Observation - Model

Aqua MODIS Clear Sky Radiance Bias: Band 36 After spectral shift Observation - Model

Brightness temperature differences as a function of scan angle.  m September February K

Band: Daytime (Low Yield) Reference lines, from Band 24 curves

Band: Nighttime Reference lines, from Band 24 curves (Low Yield)

Observations: For scan angles less than ~30 degrees, both AIRS and MODIS show scan asymmetry which is most likely due to local time differences from one side of the scan to the other. For AIRS and for band 24 of MODIS, this behavior has the same character for all scan angles (i.e. the linear behavior of the scan asymetry plots as a function of scan angle). For MODIS, the scan angle asymmetry has a different character at larger scan agles, beginning around 30 degrees off nadir, which is more pronounced for the longer wavelengths bands. For Band 24 Daytime, AIRS and MODIS show nearly the same scan asymmetry, yet still have differences (AIRS- MODIS) which increase with scan angle (i.e. the differences increase almost linearly with scan angle). More asymmetry is seen at night.

SWIR 5um Leak Correction: Terra Aside 2 Terra MODIS Aside 2 nighttime B26 data reveals artifacts of thermal band features even after the 5um thermal leak correction has been applied in L1B. Aqua MODIS shows practically no features. Up to 1% (of Ltyp) effect in Terra MODIS B26. What about other Terra SWIR bands (B5-7)? We don’t have routine nighttime data to inspect.

Aqua MODIS Day UTC 7.3 um 1.38 um Hurricane Jeanne

TerraMODIS Day UTC 7.3 um 1.38 um Hurricane Jeanne

MODIS Emissive Band Destriping Update: Granule vs. Global Analysis The Atmosphere Group products for collection 5 include destriping of all emissive bands (20-25, 27-36) and band 26. The destriping algorithm is granule-based, and for a small percentage of granules, the impact may be equivocal in bands 31 and 32. Granules with sharp transitions between warm and cool scenes (e.g. hot land, cool ocean) may have artifacts in the scene transition zone. We analyzed a complete day of data (Terra MODIS , collection 5) to develop the destriping LUT for bands 31 and 32, with the expectation that sampling a wider range of scenes would remove the artifacts.

Terra MODIS UTC (South Australia) Band 31 - Band 32 Difference, No Destriping Land Water Cloud

Band 31 - Band 32 Difference, Granule-Based Destriping

Band 31 - Band 32 Difference, Daily-Based Destriping

Backup slides

Band 36 (14.2  m) Scan Angle Asymmetry 382 non-polar clear swaths Borrowed from Dave Tobin, SSEC, UW AIRS cross track profile is approximately symmetric