1. PATMOS-x expericences in the recalibration of the AVHRR series Andrew Heidinger NOAA/NESDIS Andii Walther UW/CIMSS

2. Project Overview

3. What is PATMOS-x?   Pathfinder Atmospheres – Extended  AVHRR Version 5.3 data (1979-present) is at NCDC and being updated daily.  NOAA/NESDIS cloud climatology and includes some other records like surface temperature, OLR, NDVI and AOT.  consists of twice daily fields from all AVHRR/s (versions 1,2 & 3)  PATMOS-x data uses AVHRR GAC.  Underlying processing system (CLAVR-x) can process GAC, HRPT, LAC in NESDIS or AAPP formats.  CLAVR-x is now available from CSPP so should be easy for anyone to download and test on 1km AVHRR data. TIROS-N AVHRR November 1978

4. Motivating the Need Without on-board calibration, calibrations of individual satellites can be inaccurate and change with time. No guarantee of satellite-satellite consistency. Some previous calibration attempts were incomplete or inaccurate for some sensors (i.e. ISCCP for morning AVHRR satellites – ISCCP now fixed). PATMOS-x’s goal was to recalibrate all data from every AVHRR sensor and to try to force satellite to satellite consistency. Greenland Target

5. PATMOS-x SolCal History  2000 – My first attempt at Desert Calibration for NOAA-12. (see refs)  2002 – Generated initial calibration of NOAA-16 AVHRR using MODIS / AVHRR SNO. (see refs)  2010 – Completed first version of PATMOS-x SolCal. Delivered to GSICS, SCOPE-CM and CM-SAF CLARA-A1. (see refs)  2010-2014 – Updated this calibration yearly  2013 – ISCCP uses PATMOS-x SolCal results to verify and correct ISCCP calibration.  2015 – Expanded and repeated calibration using new C6 MODIS data.

6. Details on calibration and/or geometric correction approach

7. Our PATMOS-x AVHRR reflectance calibration is based on 4 (now 6) different sources of data. Method has been applied to all sensors. 1.MODIS to AVHRR SNO (2000-2009) 2.AVHRR to AVHRR SNO (1980-2009) 3.Libyan Desert MODIS-derived Reference 4.DOME-C MODIS-derived Reference Images courtesy of CEOS and STAR websites DOME-C Libya SNO AVHRR Reflectance Calibration Methodology (circa 2010)

8. Update of PATMOS-x AVHRR Solar Reflection Calibration in 2014  The PATMOS-x solar calibration (SOLCAL) is tied to calibration of the NASA Moderate Imaging Spectroradiometer (MODIS).  MODIS released a new calibration that fixes any known issues.  The PATMOS-x SOLCAL was regenerated with the new C6 MODIS data.  The new PATMOS-x calibration includes more earth targets to increase its accuracy.  We now use our Level-2b data (sampled level-2) instead of Level-3 (averaged level-2).  We include the 1.6 micron channel and METOP-A.  New calibration data delivered to the Global Space-based Inter-calibration System (GSICS) and to the WMO SCOPE-CM AVHRR Project. 8 Example plot generated to monitor AVHRR PATMOS-x v5.3 CDR deliveries to NCDC.]

9. PATMOS-x SolCal Earth Targets Ideal sites are radiometrically stable, dry and viewed by all AVHRR satellites in a manner similar to MODIS. Original sites where Libyan Desert and DOME- C Takliman added to dry and have a “dryer” desert than Libya. Greenland added to get more points than DOME- C. Polar sites are illuminated for short period per year. LIBYA GREENLAND DOME-C TAKLIMAKAN

10. Libyan Desert Reference Reflectance 1.6  m 0.86  m 0.65  m Reference is based only on nadir views (<15 degrees). Solar zenith angle range obtained by using data over all year. MODIS reflectance divided by ozone and water vapor transmission. Transmission comes from CFSR data and MODTRAN.

11. MODIS/AVHRR SNO Example Images show a single SNO event between TERRA- MODIS and NOAA- 19 AVHRR. Day 220 2013 between the descending node of TERRA and ascending node of NOAA-19 Red points show those that met the time/angular criteria. The mean of the red points are saved and used as one calibration point. MODIS Ref. AVHRR Count Scatterplot shows the 22 SNO points used from 2013 season (days 180- 220) for NOAA-19 and TERRA. One scatterplot yields one calibration point.

12. Example Result: METOP-02, Ch1

13. Thermal Calibration in PATMOS-x  We don’t use the level-1b calibration coefficients due to known errors and the lack of consistency over time.  Use the “HRPT” calibration – based on PRT, space counts and negative space radiance terms (Walton et al.)  We do smooth the scan-line values of the calibration slopes to mitigate impact of transients.

14. Geolocation Correction in PATMOS-x  No recent progress  Fred Nagle developed a routine to apply clock corrections and to modify geolocation.  Very quick routine  Ignores yaw and pitch errors – only along track correction  Clock errors come from University of Miami – Ocean Pathfinder.  Need method to deduce clock errors independently.  Database and code delivered to SMHI / CM-SAF

16. Main Challenges  Major:  Once MODIS is gone, need to develop new methods for VIIRS since VIIRS lack 0.94 micron channels used for water-vapor adjustments for Ch2.  Poor GAC coverage of TIROS-N, NOAA-6 and 8 is major issue for calibration of these satellites.  Technical:  DOME-3 results show wider spread than Greenland – reason unknown.  Small annual cycle in Ch2 slopes from Libyan Desert seen. Maybe a new reanalysis source or RTM model would help reduce this.

17. Conclusions  PATMOS-x AVHRR calibrations seems to be accepted and used by multiple groups. No major artifacts reported.  New C6 version solves problems due to degradation in MODIS in the C5 calibration.  New Version includes 1.6 micron channel and METOP-A.  We hope to continue to expand the sources of data used in the PATMOS-x SolCal and interested in collaboration.

18. Future Plans  Combine AVHRR & HIRS  NCDC is funding us to merge AVHRR and HIRS for a new version of PATMOS-x.  Includes mapping 6.7 and 13.3 micron channels into AVHRR projection.  Allows more spectral consistency from AVHRR-MODIS-VIIRS  Should aid in AVHRR Thermal Calibration (?)  UW/SSEC colocation code should work with European HRPT.  Extend to GOES (1994-present).  PATMOS-x GOES is technically possible (CLAVR-x runs on GOES) but we need to apply the PATMOS-x calibration techniques.

19. Don’t Forget the European NOAA LAC Archive  10% of every AVHRR orbit is stored on tape at full resolution (LAC)  Typically, there are approximately 200 LAC orbits over Europe per month.  Data archives seem empty before 1986.  CIMSS can help provide this. Data format is NESDIS format Level-1b. (Similar to AAPP) NOAA/CLASS Map of LAC data over Europe on Jan 1, 1990

20. Thanks! Backup Material Follows

21. Experience with MODIS and MSG/SEVIRI have shown that channels in IR absorption bands of h 2 o (6.7  m) and co 2 (13.3  m) are important for cloud remote sensing. 8.5  m is also important for phase and microphysics. AVHRR provides a long record spanning since 1981 but lacks the 6.7, 8.5 and 13.3  m channels. HIRS provides 6.7 and 13.3  m channels but at a coarser spatial resolution. HIRS provides only limited 8.5  m observations. HIRS + AVHRR can provide a consistent spectral basis comparable to MODIS and SEVIRI from 1981 to present. AVHRR + HIRS MODIS or SEVIRI AVHRR tropical transmission spectra channel response Spectral Consistency For Stable Multi-decadal Cloud Climate Records 21

22. Example of HIRS mapped to AVHRR 22 NCDC is funding us to make a HIRS + AVHRR CDR. We have 10 years of AVHRR + HIRS calibrated Level-1b. Inclusion of HIRS should provide opportunities for AVHRR thermal calibration correction or monitoring. Format is HDF4 Supported by CLAVR-x

23. References  Desert Target Processing:  Heidinger, A. K., J. T. Sullivan and N. Rao, 2003: Calibration of visible and near-infrared channels of the NOAA-12 AVHRR using time-series of observations over deserts. I.J.R.S., 24, 3635-3649.  MODIS SNO Processing:  Heidinger, A. K., C. Cao, and J. T. Sullivan, Using Moderate Resolution Imaging Spectrometer (MODIS) to calibrate advanced very high resolution radiometer reflectance channels, J. Geophys. Res., 107(D23), 4702, doi:10.1029/2001JD002035.  PATMOS-x C5 Calibration  Molling, Christine C.; Heidinger, Andrew K.; Straka, William C. III and Wu, Xiangqian. Calibrations for AVHRR channels 1 and 2: Review and path towards consensus. International Journal of Remote Sensing, Volume 31, Issue 24, 2010, pp.6519-6540.  Heidinger, Andrew K.; Straka, William C. III; Molling, Christine C.; Sullivan, Jerry T. and Wu, Xiangqian. Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record. International Journal of Remote Sensing, Volume 31, Issue 24, 2010, pp.6493-6517  PATMOS-x Reference  Heidinger, Andrew K.; Foster, Michael J.; Walther, Andi and Zhao, Xueping (Tom). The Pathfinder-Atmospheres Extended AVHRR climate dataset. Bulletin of the American Meteorological Society, Volume 95, Issue 6, 2014, pp.909-922.

24. Meeting Guidance  1) Data consolidation talks  - two slides: project overview  - three slides: details on data consolidation approach  - two slides: main challenges  - conclusion slide  2) Calibration/Geometric corrections  - two slide: project overview  - three slides: details on calibration and/or geometric correction approach  - two slides: main challenges  - conclusion slide  3) Experiences with AVHRR 1km  - two slide: project overview  - three slides: what data is currently used (processing level, source, space/time domain, calibration-navigation, etc)  - two slides: design your ideal data set (processing level, data format, documentation, etc), and what is MOST important?  - conclusion slide  ————  Seed questions for the meeting:  - What processing level do you recommend for a harmonised AVHRR multi-sensor dataset? Level-1a or Level-1b?  - Which Level-2 products would most benefit AVHRR Level-1 diagnostics?  - What technical barriers do you see for a multi-sensor 1-km AVHRR dataset? The calibration? The geometric accuracy?  - What approaches and algorithms are most appropriate to process the data?  - What is required (essential) for the multi-sensor dataset in issues of documentation and formats?  - What level of effort/technical barriers do you foresee (for the essential/necessary activities) to achieve a harmonised AVHRR multi-sensor dataset?  - What data policy challenges do you foresee to achieve a harmonised AVHRR multi-sensor dataset? What would be a good compromise to achieve the most complete 1-km AVHRR Level-1 dataset?

25. Other Calibration Specifics  We do use the space views to look for lunar views and through this data out.  We do test the Ch1 counts during the “night” to look for solar contamination (very prevalent for NOAA-15).  We also convert the dual-gain counts into single-gain counts as described in Heidinger et al. (2010). Seen no evidence that this is harmful.  When PRT values are missing or out-of-order, we skip this data.

26. SatelliteChannel 1Channel 2Channel 3a S0S0 S1S1 S2S2 S0S0 S1S1 S2S2 S0S0 S1S1 S2S2 noaa-06 0.087 22.164 -0.935 -0.059-395.058 117.103 0.000 noaa-07 0.114 5.492 -0.585 0.126 6.801 -0.761 0.000 noaa-08 0.056 249.052-103.086 0.130 -11.614 47.920 0.000 noaa-09 0.108 6.657 -0.082 0.120 5.340 -0.473 0.000 noaa-10 0.103 12.183 -2.010 0.134 3.679 -0.270 0.000 noaa-11 0.112 -0.876 0.270 0.118 -0.238 0.225 0.000 noaa-12 0.129 1.593 -0.130 0.151 2.958 -0.255 0.000 noaa-14 0.118 5.493 -0.549 0.150 2.349 -0.044 0.000 noaa-15 0.123 -0.633 0.031 0.139 0.248 -0.010 0.000 noaa-16 0.112 0.427 -0.003 0.117 2.392 -0.269 0.120 -12.115 4.462 noaa-17 0.117 0.199 0.093 0.132 3.635 -0.322 0.124 2.040 -0.123 noaa-18 0.111 2.058 -0.121 0.124 2.655 -0.1550.000 0.00 noaa-19 0.108 2.247 -0.317 0.121 1.693 -0.132 0.000 metop- 02 0.112 0.906 -0.024 0.133 0.814 0.025 0.126 2.020 -0.115 metop- 01 0.119 -7.442 2.828 0.116 10.222 -3.271 0.141 -27.970 10.925 Preliminary Calibration Results (9/29/14)