NOAA AVHRR Data Curation and Reprocessing - TIMELINE
Bibliographic record
Abstract
2013 marks the 35th anniversary of NOAA's Advanced Very High Resolution Radiometer (AVHRR) first launched in 1978. The four to six band multi-spectral AVHRR data constitute a valuable data source for deriving time series of surface parameters, such as snow cover, land surface temperature, or vegetation indices for monitoring global change. AVHRR data are residing in various archives worldwide. ESA has archived NOAA data in several of their facilities; DLR has been receiving and processing NOAA AVHRR data into value-added products since 1981. In order to properly preserve this valuable dataset, ESA has initiated the NOAA AVHRR data curation and reprocessing initiative as a pilot project within ESA's long-term data preservation (LTDP) program. The project's objective is the consolidation and curation of a temporally and geographically complete and consistent dataset of 1 km NOAA AVHRR HRPT data over Europe and Canada. The project will be conducted jointly with the members of the LTDP working group of the ESA Ground Segment Coordination Body. Additionally, in 2013 DLR has kicked off TIMELINE, a project focusing on generating a range of over 25 thematic products from the complete coverage of 30 years worth of NOAA AVHRR 1 km level 0 data over Europe. The thematic products will serve as input for performing higher level time series analyses on land surface dynamics, such as investigating changes in annual snow cover duration or exploring local land surface temperature trends. Within the project a sustainable, generic infrastructure for processing Earth observation time series at DLR will be implemented. With their wide scope, the two projects touch upon a variety of topics and disciplines on the scientific side as well as in large volume data processing and data management. The initial challenge lies in identifying and closing temporal and geographic data gaps for consolidating and curating 30 years worth of AVHRR data held in dispersed archives. Curation will be done in line with the European LTDP data preservation workflow. Preparation for systematic pre-processing and processing will involve external calibration and navigation data to be introduced in the pre-processing systems. Turning scientific algorithms into flexible processing systems to efficiently re-process 30 terabytes of level 0 data into about 50 terabytes of validated thematic products is another challenge. On the data management side product archiving and access infrastructures will have to be adapted to ensure e.g. efficient and user-friendly retrieval of localized time series data stacks. Interactive on-the-fly visualizations of time series products will facilitate the understanding of complex and interacting temporal phenomena. Close collaboration between scientists and ground segment engineers - and their respective operational approaches - is a particularly useful aspect of this project, with a view to expanding ground segment services towards incorporating flexible, large volume scientific processing chains in the future. From the NOAA AVHRR Data Curation and Reprocessing and TIMELINE projects novel ideas for managing large Earth observation time series data sets are expected across the entire end-to-end chain - from data consolidation via reprocessing and data management to innovative ways of data discovery and exploitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".