Objectives

1. AVHRR Stewardship ProjectPathfinder Atmospheres – Extended (PATMOS-x)Andrew Heidinger, Aleksandar Jelenak, Michael PavolonisNOAA/NESDIS/ORA

2. Objectives • Improve the AVHRR data quality (notably geolocation and reflectance calibration) • Use PATMOS-x mapped data as vehicle to provide improved AVHRR data and selected climate data records (similar to level 3 MODIS products) • Use the spectral information and spatial resolution offered by AVHRR to expand the knowledge available from existing climatologies (ISCCP and HIRS) • Develop cloud climate records from the AVHRR that are physically consistent with those from MODIS and VIIRS

3. Scientific Rationale (Relevance to Climate) • The AVHRR provides a unique 25 year record of global data from a consistent set of sensors. Underutilized for cloud climate research. • Past attempts (i.e. PATMOS) have shown the need for the data improvement activities were undertaking. • Results from existing cloud climatologies differ in some key respects and the unique information provided by the AVHRR may help bring consensus. • The scientific relevance of the cloud climate records from EOS and NPOESS will be much larger if we can extend selected time series back in time using the AVHRR data.

4. Current Internal Capabilities and Activities • PATMOS – The AVHRR Pathfinder Atmosphere project (1992-1998). Processed only afternoon AVHRR data (1982-1999) into 1° climate data records. Included cloud amount, aerosol over ocean and OLR and planetary albedo. No other cloud products. Data hosted by SAA/CLASS • CLAVR-x – NESDIS’s new operational cloud processing system developed by ORA. Replaces CLAVR with algorithmic and processing improvements over the last few years. For example, CLAVR-x algorithms can account for terminator conditions in morning orbiter AVHRR data. Includes full suite of cloud products (layered amounts, cloud type, cloud temperature, optical depths, particle size, LWP, IWP, …). • PATMOS-x – (an AVHRR reprocessing using CLAVR-x) was developed as a pilot project funded by ORA. PATMOS-x runs CLAVR-x in a configuration that uses ancillary data and processing steps to transition its EDRs into CDRs.

5. The ORA AVHRR Processing System (as used for PATMOS-x) INPUT PROCESSING OUTPUT (0.5°) 12 TB of online storage 10 dual CPU Linux Workstations • Orbital pixel-level products • Used for GVI-x processing Orbital Mapped Products • AVHRR GAC Level 1B from CLASS (65 MB/ 32 TB)* • Clevernav – new pixel • geolocation (10 MB / 2 TB) • NCEP Reanalysis (50 GB) • Clear-sky Ch1 and NDVI Composites (1 MB / 3 GB) • OISST (0.5 MB / 700 MB) CLAVR-x** OPTRAN Mapping Averaging Daily Mapped Files (50 MB / 2TB) Monthly Averages (50 MB / 45 GB) * Size given per orbit (or day) processing and for processing all orbits or days. **Note, CLAVR-x is same system used by OSDPD but run in different configuration for PATMOS-x

6. Results – Temporal Sampling (High Cloud Amount ) • We have tried to build a system that captures the needed temporal/spatial resolution for cloud climate research. One of the big concerns using AVHRR for climate is orbital drift. • Sensitivity to orbital drift is mitigated by processing all data (am,pm,terminator) • Seek algorithmic solutions that are consistent from satellite to satellite (inter-annual) and for all viewing geometries (seasonal) Diurnal Cycle: July 2004 NOAA-15,16,17 Seasonal Cycle: NOAA-16 Des. 2004 Inter-annual Cycle: July 1982-2004

7. Results: Comparison of PATMOS-x time series with others (High Cloud Amount) • Note time series differ in magnitude and signs. • ISCCP plagued by difficulties high cloud detection at night • AQUA similar to PATMOS-x but new version of AQUA is forth coming

8. More Results – Non-cloud PATMOS-x products • PATMOS-x contains multi-discipline CDRS. For example, PATMOS-x contains aerosol optical depth and NDVI. • While we recognize the GVI-x is no doubt a better NDVI time series for climate studies, having an NDVI produced from PATMOS-x provides a strong diagnostic tool for the performance of cloud processing (i.e. cloud mask). Also, the PATMOS-x NDVI product may be sufficient/convenient for many cloud/surface process studies. 0.63 mm Aerosol Optical Depth NDVI (Atmos. Corr.) Monthly Variation from 2004 from NOAA-16

9. Results: Contributions of PATMOS-x to AVHRR Data Improvement • Using Simultaneous Nadir Observations (SNO) to derive a MODIS based AVHRR Reflectance Calibration. • SNO’s also used to tie reflectance calibration of overlapping AVHRRs together MODIS Ch1 Reflectance AVHRR Ch1 Count Ref. Cal. produces continuous Greenland reflectance time series (bright surface). Ref. Cal. Produces continuous AOT time series (dark surface). We are still bothered by difference between MODIS-based and traditional results (N.Rao)

10. Results: Contributions of PATMOS-x to AVHRR Data Improvement • Aleksandar Jelenak has developed a tool to reduce the geolocation errors due to: • AVHRR clock errors • Interpolation from the sub sampled anchor points in Level 1B • This tool produces hdf files meant to accompany the Level 1b data. AFTER BEFORE

11. Issues with current approaches (Why do we need PATMOS-x given the existing cloud climatologies?) • ISCCP’s use of a single reflectance and single thermal channel has introduced large day/night differences in some critical cdrs. While temporal resolution (3 hrly) is a strength of ISCCP, the day/night differences in the approaches negate the ability for diurnal sampling for some products. • Some of ISCCP’s cloud amount time series show trends not seen in other climatologies. ISCCP was never designed for long term studies. • HIRS based cloud climatologies do not suffer from the day/night differences but lack sensitivity to low clouds. Low spatial resolution (20 km) prohibits resolution of some types of cloudiness. Example HIRS climatologies are the UW-NOAA HIRS CO2 Slicing (Menzel, Wylie, Bates, Jackson) and Others (Stubenrauch, Susskind).

12. Recommended Approach (Activities) Continue to develop the PATMOS-x Cloud Climatology: • Finish MODIS-based Reflectance calibration. Support other ORA efforts at FCDR improvement • Reprocess all AVHRR data through PATMOS-x • Analyze PATMOS-x data. Reprocess as we learn how to improve its time-series (leverage off PEATE, GOES-R) • Participate in GEWEX Cloud Working group and other collaborations and try to reach a consensus among members and gain acceptance/build maturity of the PATMOS-x data. • Continue to publish core PATMOS-x algorithms to facilitate confidence in the climate community.

13. Reducing Uncertainties • Come to a consensus on the AVHRR calibration and quality assurance approaches. Use PATMOS-x as a calibration test-bed. • Pursue approaches that result in stable long term cloud climate data records. Use advanced sensors to characterize performance (CloudSat and CALIPSO). • Leverage off the validation efforts for MODIS and NPP which occur while the AVHRR is still flying. Strive to achieve consistency between analogous products.

14. External Collaboration • Participant in recent GEWEX cloud climatology assessment workshop • Participant in NPP PEATE which seeks consistent cloud cdrs from NPP/VIIRS and AVHRR • Collaborating on Reflectance Calibration with N. Saleous (NASA). Geolocation improvement method handed to Eumetsat CM-SAF (P. Albert). • PATMOS-x cloud property algorithms used by M Uddstrom from NIWA and leveraging off of his validation. • Proposal submitted with Steve Platnick to pursue continuous records in cloud optical thickness and particle size from AVHRR/MODIS • PATMOS-x cloud typing routine used by NPOESS contractor

15. Deliverables • FY05: An AVHRR local archive in ORA, a prototype cloud climatology system (PATMOS-x), geolocation improvements. • FY06: A complete reprocessing of the AVHRR GAC data through PATMOS-x. Analysis of time series. Completion and validation of new reflectance calibration (MODIS-based). • FY07: Validation of time series from PATMOS-x and algorithm refinements to maximize maturity. Conduct final reprocessing. Explore merger with HIRS.