Initiated by ECSN, sponsored by EUMETNET (Functional) activities –Gather high quality data with daily resolution –Apply quality control procedures –Analyse data for climatological purposes –Disseminate data 45 participants, 41 countries, 257 locations 992 participant series (time continuous) synoptical data series (partly time continuous) Daily max temp, min temp, mean temp, pressure, precipitation Dating back to 1775, in general most data from about 1875 European Climate Assessment & Dataset: ECA&D

Design of relational datamodel Starting-points datamodel: Should store raw data, derived data and attributes of data Should be easy to add new (or updated) data Should be easy to relate one data entity to another data entity Should enable new applications (relational, analytical) Should discriminate several levels of access to data and analyses Should ascribe levels of data quality and useability (QC flags, homogeneity) Should be functionaly expandable

Storing as-is data Participant “John Doe” Providing maximum temperature series, recorded at 12 GMT At station Fort Bourke Participant IDParticipant nameParticipant cityParticipant country 12John DoeDuckstadFederal Feather Element IDElement nameDescriptionUnits 7TX2Maximum temperature, recorded at 12 GMT0.1 ºC Station IDStation nameLatitudeLongitudeHeightDetails 312Fort Bourke Soil, high trees at SW 150 meters Series IDParticipant IDElement IDStation IDPublic Y Series IDSeries dateSeries valueSeries Quality Control Flag

Linking together the identifiers Storing the data itself Participant Station Element Data series Series table Series identifier Series date Series value Series quality control

Processing data Blending data (example next slide) Climatological analyses on data Quality control flags Homogeneity Trends in indices

Blending data Create time continuous, up-to-date data series (fill gaps, and extend series) ECA station Other station (< 50 km, < 50 m) Synop data Continuous, up-to-date series t=0t=now Data from participants: validated, but not up-to-date Data from synops: up-to-date, but not validated  Daily quality control procedures inevitable!  But, participant data still needed! Location <= 50 km > 50 m

Identifying blended data series Take a ECA station to represent the blended (group) data Identify the blended data series with a unique number (linked to ECA-station) Store the used sources for blending Series IDSeries QC flagSeries valueSeries dateLocationID ‘core’ ECA-series Nearby Synop-series Nearby ECA-series Nearby Synop-series Continuous, up-to-date series t=0t=now Series IDParticipant IDElement IDStation IDPublic Y Fort Bourke312 LocationIDHeightLongitudeLatitudeStation nameStation ID Details Soil, trees...

Blended data series Nearby stations Data series (of participant) Synops Storing blended data series Table series_blended Indices Table series_indices Trends Table series_trends Homogeneity Table homogeneity

Climatological analyses 40 indices calculated (currently for 257 locations) Based on blended data series Very up-to-date, thanks to blending Examples: Mean of daily temperature Growing Season Length Highest 1-day precipitation amount

Quality control and homogeneity QC applied on participant data series, and blended data series. Examples: Tx >= Tn Temp between window of 5 times standard deviation Precipitation not too repitative Homogeneity results calculated with: Regarding temperature series:  Combined results of 4 homogeneity tests on two indices (DTR and vDTR): Regarding precipitation:  Combined results of 4 homogeneity test on index RR1 (threshold 1 mm): 4 homogeneity tests:Standard Normal Homogeneity test, BuisHand Range test, PETtit test, Von NEUmann test

Summary Daily updates of data Calculation of quality on a daily basis In principle: indices, trend, quality control, homogeneity are calculated for all available data In principle: all data is downloadable Database easily expandable by using a flexible data model at