The CarboeuropeIP Ecosystem Component Database: data processing and availability Dario Papale, Markus Reichstein.

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Presentation transcript:

The CarboeuropeIP Ecosystem Component Database: data processing and availability Dario Papale, Markus Reichstein

Database Typically, the “bilateral meetings” talk…. Why do we need a database?

Knowledge in Eddy Covariance Knowledge in Modeling and data synthesis Modelers Flux people

What is it needed? Data, provided by the PIs and shared with the scientific community Data have to be with good quality (the user are not able to understand this looking to the dataset…) Data have to be in the same format Data have to be “ready to be used”

Info used in the to the PIs

And also the list of downloads from your site…

Data quality centralized checks Why do we need to correct the data if the PIs correctly apply all the quality checks? How many PIs are applying these tests/corrections correctly? How many PIs are reprocessing old data with new methods? and…..

Level 0.x Level 1.x Level 2.x Level 3.x Level 4.x Conversion (Site) QC I (Site & DB) QC II (DB) (semi-automatic) GF/FP/aggregation (DB) (automatic) Datasets available on the database Different levels of products and different versions Two steps quality checks

BitsDescriptionDefinition of values 9898 QC from PI Marginal point 0: ok (class 0-1 of QA/QC method based on raw data – Foken et al.) 1: not ok (class 2) 0: no; 1: marginal point 7-6u* criterion00: ok 01: below treshold 10: preceeding below threshold 11: below treshold and preceeding below threshold 5-4Spike detection00: no spike 01: spike as outside 4 SD 10: spike as outside 5.5 SD 11: spike as outside 7 SD or out of range. 3Low variability0: ok; 1: low variability (e.g. when pump broken etc.) 2-1Summary00: best 01: medium 10: bad 11: missing 9 bits QC flag

QC II for level 3 products Fluxes -Marginal or isolated points -Spikes -Low variability periods Storage correction -With the storage measured using a profile system -Using the top-of-tower CO 2 conc. u* filtering Rg vs Potential radiation Rg vs PPFD ustar vs wind speed

Global radiation vs PPFD

Spikes detection method NB: these are low frequency spikes, different from the raw data spikes!!!!

Standardized ustar filtering

M x Ustar threshold selection method x selected as threshold if flux(x) >= M x 0.99 This is done for 6 different temperature classes (only if no relation between T and u* are found) and for 4 season. Seasonal ustar is calculated as median on the 6 thresholds, for annual u* the maximum seasonal value is taken

ITRo1 2000

BEVie 2002

NLLoo 1998 – storage with discrete approach

NLLoo 1998 – storage with profile

ITLav 2002

BEVie 2002 … then we fill gaps with two methods (ANN and MDS) and calculate GPP and TER …..