Progress in Level 1 work (calibration + navigation AVHRR GAC/LAC) ESA LTDP AVHRR LAC meeting DLR, Munich April 2015 Karl-Göran Karlsson, Abhay Devasthale, Martin Raspaud SMHI, Norrköping, Sweden Öystein Godöy Norwegian Met. Institute, Oslo, Norway

2 Current projects and activities ’Normal’ HRPT/LAC reception and processing in Norrköping and in Oslo for operational met/hydro/ocean forecasting/monitoring services: HRPT  AAPP + ANA  Level 1b data  higher level products Additional activities linked to EUMETSAT SAF Network: -Development of AVHRR cloud processing package PPS in the Nowcasting Satellite Application Facility (NWC SAF) -High-resolution ice and SST mapping + surface radiation fluxes in Ocean and Sea Ice Satellite Application Facility (OSI SAF) + NORMAP + CryoClim Interesting links to global AVHRR (GAC) processing: -Clouds, Surface albedo and surface radiation products (CLARA dataset) in Climate Monitoring Satellite Application Facility (CM SAF) -Cloud products in ESA-CLOUD-CCI project -SCOPE-CM project ”Advancing the AVHRR FCDR”

3 pyGAC – Python module for Level 1c processing of AVHRR GAC/LAC data: pyGAC development * * Developed jointly by CM SAF and ESA- CLOUD-CCI projects Recently extended with LAC processing capability Including latest upgrade of visible inter-calibration (Heidinger, 2014, pers. comm.) Improved stability and accuracy Applied corrections for clock errors (Univ. Miami) + prepared for extended navigation corrections Improved handling of corrupt data

4 Example of LAC over Italy using pyGAC

5 Reflectances in AVHRR ch 1 from pyGAC (Provided by Cornelia Schlundt, DWD)

6 Ongoing pyGAC work: Improved navigation Motivation: Clock errors only along-track + missing for morning satellites for POD series Clock drift error estimation 1 Use a global reflectance map, remapped to the swath 2 Correlate the along track signals from cloud-free data with the reflectance map 3 Find peak Attitude correction * Use a remapped global reflectance map, generate landmarks (Khlopenkov & Trishchenko 2008) * With 5 landmarks, attitude error can be estimated * Use time series to validate attitude estimation

7 Challenges and conclusions Challenges: -Full European and Arctic coverage (merging of datasets) -Efficient quality control (corrupt data, data gaps, etc.) -Continued improvement of visible calibration -New infrared calibration (FIDUCEO) -Very accurate navigation (current efforts remove large errors) Conclusions/Recommendations: -Large progress achieved through international collaborations -Data format standardisation (netCDF) important for higher level processing  Should be considered also for Level 1!