A Fundamental Climate Data Record for the AVHRR

1. A Fundamental Climate Data Record for the AVHRR Jonathan Mittaz Manik Bali & Andrew Harris CICS/ESSIC University of Maryland

2. Funded project through NCDC for 3 years (part of NOAA Climate Data Record Program) Goal To provide recalibrated AVHRR Level 1B radiances for the thermal IR channels (3.7, 11 and 12 μm channels) which are as accurate and bias free as possible and where the uncertainty on the radiances are better understood. Source Data NOAA AVHRR Level 1B data Deliverables – not yet fully defined by likely to be one or more of Code to calculate new radiances from current AVHRR Level 1B files Recalibrated Level 1B data files (all AVHRRs in KLM format) NCDC specific format (netCDF for example) Part of SW/IR Imager FCDR Team – Team Lead : Bob Evans AVHRR IR CDR Project

3. Use a physically meaningful calibration algorithm (current operational calibration (Walton et al. 1998) is not) Apply a uniform calibration methodology to the complete AVHRR data record Current AVHRR Level1B data have a changing calibration methodology over time. Walton et al. calibration is available for NOAA-7,9,10,11,12,14 and all AVHRR/3s but is significantly biased. Reanalyze AVHRR pre-launch data to obtain instrument non-linearity Calibration Algorithm

4. Pre-launch Data Calibration Test Chamber Calibration Targets - ECT (180->320K) & Space Target @ 70K No thermal shielding – very simple test chamber. Future pre-launch tests should be done better Run at 5 instrument temperatures of 10, 15, 20, 25, 30°C

5. Example of pre-launch problems (Mittaz, Harris & Sullivan) Pre-launch Data (2) Application of the Walton et al. calibration on the pre-launch data from which it was derived shows large biases – sign of severe problems with the pre-launch data and methodology Can be fixed by the use of a physically based methodology – means that all pre-launch data has to be re-analyzed Some pre-launch calibration parameters will still be corrupted

6. Use Physically based calibration equation (pre-launch and TOA) Use Top Of Atmosphere calibration sources (e.g. (A)ATSR, IASI etc.) when available to correct parameters contaminated during pre-launch testing (underlined in red) Use model of instrument to obtain calibration when contamination exists (solar contamination) when possible Remove periods of bad calibration from record Monitor calibration as a function of time and correct when necessary Calibration Approach (TOA)

7. Operational and New Calibration comparisons (IASI) AVHRR calibrated by operational scheme – shows large temperature dependent biases Combination of incorrect algorithm and pre-launch contamination Correct by fitting corrupted calibration parameters to a TOA calibration radiance source (in this case IASI)

8. Assessment of AVHRR/3 stability over 6 months - stable (<0.1K) MetOp-A AVHRR Stability 220-230K Even operational calibration has constant biases to 0.05K 290-300K New calibration shows small trends at the < 0.08K level (note change in scale wrt previous plot by ~ factor 10) 220-230K 290-300K

9. MetOp-A AVHRR stable over 3+years • Now done longer term study – MetOp-A AVHRR stable over 3+ years. Close to climate change requirements (Ohring et al. 2004) Accuracy = 0.1K Stability (per decade) = 0.04K SST Data (>270K) 11 µm Bias = 0.03K Gradient = 0.014 K/decade 12 µm Bias = 0.03K Gradient = 0.004 K/decade

10. MetOp-A AVHRR Thermal Trend Small drift in average orbital temperature (0.2K in 4 years) with clear seasonal variability Constancy of temperature may in part explain stability of AVHRR calibration 0.2K

11. Use of the AATSR as a TOA Calibration Source • Baseline instrument for re-calibration is the (A)ATSR series • Designed to be climate ready • Accurate and stable to < 0.05K (apart from the AATSR 12µm channel see later) • Data available from 1991 to present day (covers the AVHRR/2 AVHRR/3 instruments) • Data available via FTP • One months worth of data ~130Gbytes – takes ~ 3 days to download • AVHRR data matched with (A)ATSR data (first attempt parameters) • Match individual AVHRR GAC ‘pixels’ • Take into account true AVHRR GAC footprint • Both AVHRR and (A)ATSR data should be spatially coherent (current limit σ<1K over ~12x12km area) • Satellite ZA agree to < 1° • Data limited to close to nadir (current limit < 10°) • For daytime 3.7µm channel keep relative azimuth angle to < 30° • Maximum time difference between AVHRR and (A)ATSR data < 10 minutes • Correct (A)ATSR data for differences in spectral response functions

12. Comparison of MetOp-A AVHRR with AATSR (IASI parameters) • Compare 11 and 12 µm channel AVHRR data calibrated using the parameters derived from IASI matches - 11µm good agreement, 12 µm not Strong trend to -0.5K at cold temperatures – highlights issues with AATSR 12µm channel (AATSR Cal Team informed) Good agreement with a slight (-0.05K) bias – small tweak can make the data match

13. AATSR/NOAA-16 AVHRR Comparison (11µm) • In Operation since March 2001 – thought to currently have a bad calibration (e.g. ‘out of family’ from NOAA MICROS pages).- test case for AVHRR near terminator/problem checking Using calibration from MetOp-A gives a trend and bias (but smaller trends than current calibration) (Data from Feb 2003) Recalibration removes trend/bias

14. NOAA-16 data taken Feb 2010 • 7 years after previous calibration now close to terminator • Shows a distinct change in the calibration – time dependent effect Bias parameters (α,α’) have larger values than in 2003 – impact of change in thermal state Data is biased and shows a trend relative to Feb 2003 calibration

15. Average Instrument Temperature (NOAA-16) Scan motor problems Unlike MetOp-A (0.2K in 4 years), NOAA-16 shows large temperature variations – becomes extreme from ~ 2008. A change in the thermal environment may explain the change in the 2010 calibration biases relative to 2003.

16. Detection and correction of bad data - solar contamination (3.7μm) NOAA-14 Uses a simple constant to fill Misses some contamination Misses some contamination Have much better detection of times of contamination – users can be more certain they are not including bad or corrupted data Also have a model for the gain for contaminated times including an uncertainty estimate Modeled gain including uncertainty estimate Better detection of events Again better detection of events

17. Detection and correction of bad data – Earthshine • 3.7 µm contaminated by Earthshine (light from Earth scattering via Blackbody in calibration) – fix with model of Self emission Nighttime Strong correlation of nighttime gain with Earth scene radiance Daytime Note predictive capability of new calibration (also can be used for solar contamination) Up to 0.25K error in daytime BT @ 295K

18. Detection and correction of bad data – Space Count contamination • Contamination of the Space clamp view (e.g. by the Moon) will make the true instrument gain be unknowable – so these events are removed To have an accurate FCDR you need to accurately remove/flag all bad data

19. For future missions - good pre-launch testing is critical and needs to be done properly In orbit comparisons against TOA reference sources is also critical to remove biases Most of the tools are in place to recalibrate AVHRR/3 series AVHRR/2 waiting on pre-launch analysis AVHRR has the capability of being used for accurate climate studies MetOp-A is currently accurate and stable Requirement for time/temperature dependence for the calibration Clear in NOAA-16 (calibration very different in 2010 compared to 2003 Constant instrument temperature -> constant calibration? (MetOp-A) Need to remove accurately remove/estimate bad data from record Tools are in place Remaining issues SRF shift needs to be included for (A)ATSR data 3.7 µmchannel Automatic implementation of Earthshine correction AATSR 12 µm channel needs to be fixed Look into using RTM data/AVHRR overlap periods when accurate TOA sources not available (pre-1991) CONCLUSION