Progress in Level 1 work (calibration + navigation AVHRR GAC/LAC)

ESA LTDP AVHRR LAC meetingDLR, Munich

20-21 April 2015

Karl-Göran Karlsson, Abhay Devasthale, Martin RaspaudSMHI, Norrköping, Sweden

Öystein GodöyNorwegian Met. Institute, Oslo, Norway

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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”

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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

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Example of LAC over Italy using pyGAC

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Reflectances in AVHRR ch 1 from pyGAC

(Provided by Cornelia Schlundt, DWD)

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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 swath2 Correlate the along track signals from cloud-free data with the reflectance map3 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

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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!