CoRP Climate Summit

1. CoRP Climate Summit J. Key M. Gunshor J. Kossin A. Heidenger T. Schriender T. Achtor T. Schmit P. Menzel E. Prins D. Wylie Steve Ackerman Director, CIMSS University of Wisconsin-Madison This is an exciting time for satellite remote sensing. We have a sufficiently long record for studies of recent climate change, and an exciting new suite of sensors to enhance our future observational capabilities.

2. Satellite-based Data Record Generation Archive Data Radiance Vicarious Calibration Analysis Algorithm Validation

3. 3 days 3 days 5+ days 7 days 3 days It all Starts with Data: in our case SSEC Noaaport Goes West (backup) sdi Goes East (backup) sdi Meteosat-5 sdi Meteosat-7 sdi Meteosat-8 Goes Pacific sdi Goes West sdi Goes East sdi Polar Wallopssdi Polar Fairbanks sdi Polar Direct sdi Modis Direct Broad 5 days RAID-5 5 days RAID-5 3 days 3 days 3 days 4 days NOAA-14 NOAA-15 NOAA-16 NOAA-17 NOAA-18 FY1D Aqua-EOS Terra-EOS Noaaport Ch1 Noaaport DVB Meteosat-5 (63 E) Meteosat-8 (0) GOES East (GOES-12) GOES West (GOES-11) GOES SH (GOES-10) Tape Archive and Tape Retrieval Data QC Workstations Meta Database/inventory 3590 tape drive 3590 tape drive CIMSS Processing Computers User Workstations

4. SSEC Data Center – Real-Time Data • Real-Time satellite Data online • GOES-12 3.5 days • GOES-11 3.5 days • GOES-10 tbd (1 min.) • MET-5 (INDOEX high res) 5.0 days • MET-8 (MSG-1) 3.5 days • POES (relay, each bird) 3.0 days • POES (flyover) 3.5 days • TERRA (MODIS) 7.0 days • AQUA (MODIS) 7.0 days • AQUA (AIRS) 7.0 days

5. Calibration Routine satellite-to-satellite cross-calibration is part of CIMSS activities. The following examples highlight ongoing intercalibration work done routinely at CIMSS.

6. Calibration MODIS spec. given by box, comparison is star symbol. MAS and SHIS data sets collected on the NASA ER-2 aircraft have been key for directly assessing MODIS L1B accuracy. Along Track Profile

7. Spectral Calibration Crosstalk Assessment Before Correction After Correction Optics Performance (mirror striping, RVS) Band 36 (14.2 um) Before Correction After Correction Brightness Temperature (K) BOS EOS Cross Track Frame Number Calibrations Detector Performance (Linearity, NEDT, Stability, Striping)

8. Calibrations Quality Assurance of Satellite Data Relative calibration accuracy can alert users to potential problems such as crosstalk, incorrect calibration coefficients, and biases that may only affect the warm or cold end of the temperature spectrum. Suitable intercalibration targets of opportunity do not occur daily, but the entire process can be automated, performed routinely, and provide users with valuable information about the quality of various satellite instruments.

9. Calibrations High spectral resolution data, such as that from AIRS, are important for vicarious comparisons between instruments that are similar, but not identical spectrally. GOES-12 (blue) and MET-7 (green) Imagers IR Window spectral response functions plotted with a sample AIRS brightness temperature spectrum.

10. Calibrations AIRS-MODIS for Band 35 (13.9 m) with nominal MODIS SRF and shifted SRF Unshifted Shifted Unshifted Shifted

11. Calibrations Comparison of JAMI IR1 brightness temperature with AIRS. Mean Difference: -0.4 K RMS Difference: 0.7 K JAMI – AIRS(K) Date

12. Calibrations Comparison of JAMI IR4 brightness temperature with AIRS. Crosstalk Correction Started Since 2006 March 15: Mean Difference: 1.1 K RMS Difference: 1.7 K Since mid 2006 July correction update: RMS Difference: 0.6 K Crosstalk Correction Updated JAMI – AIRS(K) Date

13. Calibrations Intercalibrating GEOs with High Spectral Resolution AIRS

14. Product Generation CIMSS generates many products from satellite observations. Examples include: • Cloud properties from GOES imager and sounder • Tropical Cyclones and Winds from GOES • HIRS cloud products (AIRS) • AVHRR cloud products (MODIS) • Biomass burning

15. Smoke Transport Across Pacific from Siberia 6 May 2003 Before GOES-11 Rapid Scan Visible Imagery (1 km) 22:07, 9 June 2002 – 00:50, 10 June 2002 Courtesy of CSU - CIRA After ONTARIO QUEBEC MODIS Rapid fire 9 May 2003 Smoke Transport Across Gulf of Mexico 9 May 2003 Wildfires in Quebec, Canada 6 July 2003 at 17:45 UTC Applications of Global Geostationary Fire CDRs in Fire/Smoke Monitoring GOES Fire Product Jun-Oct 1995 GOES Smoke Coverage

16. Global Geostationary Fire Climate Data Records • Current Status and Capabilities: • The GOES WF_ABBA was developed by UW-Madison CIMSS with NOAA and NASA funding. The processing system includes quality assurance monitoring and reporting. The software is being modified to provide additional meta data regarding cloud coverage, block-out zones, image quality and availability. • CIMSS has produced half-hourly GOES-E/-W WF_ABBA fire products in near-real time since 2000 for a broad user community with on-line data access at CIMSS and the FLAMBE archive (http://www.nrlmry.navy.mil/flambe/index.html). • The GOES-E/-W WF_ABBA has been running operationally at NOAA/NESDIS/SSD since 2002. • CIMSS is adapting the WF_ABBA for global fire monitoring (Met-8, MTSAT-1R, FY-2C, INSAT-3D, etc). Transition of the Met-8 and MTSAT-1R WF_ABBA to NOAA/NESDIS operations in January 2007. • GOES-E data from 1995 – 1999 is being reprocessed with the WF_ABBA to create a long term geostationary fire climatology for the Western Hemisphere (1995 – current). (NASA LBA effort)

17. Global Geostationary Fire Climate Data Records • Scientific Data Stewardship Requirements: • Operational quality assurance of ingested data and routine monitoring of data quality and provenance during production- Many of the required quality assurance analysis and reporting capabilities already exist in the WF_ABBA processing system, including monitoring the image size, format, noise, and overall multi-band image quality. - Additional information regarding the satellite image quality/availability, viewing/monitoring capabilities (cloud coverage, block-out zones)and impact on derived fire products is currently being integrated into meta data that will be disseminated along with the fire product. - WF_ABBA fire product confidence levels are provided. • Generation of authoritative, long-term records - CIMSS has participated in both ground truth validation efforts (Canada, U.S., Central America, South America) and multi-sensor intercomparison studies (MODIS, AVHRR, ASTER) to evaluate and enhance the GOES WF_ABBA. This work will continue and expand globally. - CIMSS participates in numerous interdisciplinary GOES WF_ABBA data analysis and application research efforts. Close collaborations with the user community enable us to address user needs and concerns and ultimately results in improved fire products. - As significant improvements are made to the WF_ABBA, it is necessary to reprocess the GOES data record. On-line access to the GOES archive at UW-Madison SSEC will greatly enhance the capability for efficient and timely reprocessing.

18. Global Geostationary Fire Climate Data Records • Scientific Data Stewardship Requirements (continued): • Generation of CDR context capable of surviving transformative migration- Each operational version of the GOES WF_ABBA is fully documented according to NOAA/NESDIS standards. This serves as the primary tool to monitor changes in the processing system and derived products. - The entire GOES WF_ABBA database is currently available on-line at (http://www.nrlmry.navy.mil/flambe/index.html). A more efficient user-friendly on-line database will be implemented to access the full geostationary fire product archive and associated meta data. This system will allow for seamless integration of the global geostationary fire monitoring data set and different versions/formats.

19. Product Generation Based on GOES Sounder Radiances Temporal Coverage: November 1997 – July 2006 Hourly Observations Spatial Coverage: 20N – 50N & 65W – 130W ~ 10Km Resolution HIGH CLOUDS (300 - 100hPa)

20. Climate Studies: Polar Satellites AVHRR and TOVS

22. Comparison of Cloud Climatologies Adjusted for Sampling Differences Raw Time Series Diurnal sampling and algorithmic differences influence heavily the time-series of cloud products. Time series of ISCCP (vis and IR), MODIS and PATMOS-x high cloud amount over land, illustrating the steps required to compare variation in each time series. Normalized about the Mean Including MODIS/AQUA (2002-2005)

23. Extended AVHRR Polar Pathfinder (APP-x), 1982-2004 Cloud Forcing, Autumn

24. Historical AVHRR Polar Winds: 1981-2004 Comparison with RAOBS indicate that the AVHRR winds usually have a lower bias than ERA40 winds but a similar RMS difference. However, when compared to LEADEX (not assimilated into reanalysis field) indicated that the AVHRR biases were much lower than ERA40. Yellow: Below 700 hPa Light Blue: 400-700 hPa Magenta: Above 400 hPa

25. Validation An ongoing program of validation is carried out to determine the uncertainty associated with retrievals

26. Cal/Val Related Activities AIRS spectral radiance validation with the UW-SSEC Scanning-HIS AIRS / GOES-10 inter-comparisons. Band 2, ~3.9m AIRS v4 T/q retrieval validation using ARM site best estimates

27. Routine Product Assessment Realtime retrievals in near-real time are compared on a routine mode. GOES-12 retrieved total precipitable water is compared with microwave and radiosonde observations over ARM site.

28. GOES-12 TPW Validation GOES-12 retrieved TPW vs. Microwave Radiometer at the ARM SGP site in Lamont, OK. Shown are both summer (left) and winter (right).

29. Product Analysis: Experience

30. GOES WF_ABBA Trend Analysis in the Western Hemisphere Multi-year trend analyses of GOES WF_ABBA fire products for the Western Hemisphere provide a tool for analyzing the spatial distribution of fires and interannual changes associated with climate variability and societal impacts. Biases associated with satellite schedules and data coverage must be addressed for accurate trend analyses. In 2004 and 2005, hurricane satellite schedules resulted in a reduction of data coverage over South America by as much as 20% at the height of the fire season.

31. Recent Trends in Arctic Winter Surface Temperature Trend: APP-x (Courtesy R. Stone)

32. AVHRR Products We have used the PATMOS-x products to show a climatological relationship between African dust storms and tropical cyclones in the North Atlantic Years with heavy dust activity have few tropical storms… …but years with less dust show an increase in cyclone numbers

33. Understanding Differences in Cloud Climatologies This comparison shows the yearly variation in the mean July High Cloud Amount in the Tropics. • AQUA and PATMOS-x agree in magnitude. • ISCCP-D2 daily value suffers from poor night-time performance. • HIRS shows a slight positive trend while PATMOS-x shows no trend and ISCCP-D2 shows a very small negative trend.

34. SSEC Data Center – Archive Data • Weather satellite archive holdings • GOES 26 Jan 1979 - present • GMS-5 9 Nov 1998 - 21 May 2003 • MET-5 (Indoex) 9 Mar 1999 – present • MET-7 9 Mar 1999 - present • MET-3 1 Jan 1993 - 1 Jan 1995 • MET-8 15 Mar 2004 - present • Global products from web • Montage Apr 1997 - present • IR composites Apr 1997 - present

35. SSEC Support to NOAA: Summary

36. SSEC Data Center- Future plans Continue real-time ingest, quality control, distribution and archive of satellite and supporting geophysical data • Maintain and update antenna and receiving equipment (8 antennas, modems, fiber, etc) • Maintain staffing levels with diverse expertise (e.g. engineering, IT, data management, science) • Modernize Data Center distribution and archive hardware

37. SSEC Data Center- Future plans Data Center Modernization • Provide more online data to scientists • Provide random access of data via ADDE • Eliminate need for tape archive through development of active archive (e.g. RAID / SANS / NAS)

38. Example Collaboration… A formal collaborative effort between CIMSS and NCDC: “A Globally Consistent Reanalysis of Hurricane Trends” A 6-month project funded by the NSF SGER program James Kossin, P.I. (CIMSS/UW-Madison) Kenneth Knapp (NCDC collaborator)

39. Goal: Construct the first temporally homogeneous global record of hurricane intensity to test the fidelity of recently documented upward trends in hurricane activity. • Accomplishments: • Constructed hurricane-centric database of geostationary IR imagery for all storms in all basins during the period 1983-2005. All imagery came from the ISCCP-B1 database and was further recalibrated using HIRS data to remove all temporal biases. • Constructed and applied a new algorithm to estimate hurricane intensity from the IR imagery. • Applied the new homogeneous data (known as the UW/NCDC record) to trend analyses. • Further collaborative efforts between CIMSS and NCDC are underway to extend the data back to 1978 or earlier.

40. Satellite-based Data Record Generation Archive Data Radiance Vicarious Calibration Analysis Algorithm Validation