Initiated by ECSN, sponsored by EUMETNET (Functional) activities –Gather high quality data with daily resolution –Apply quality control procedures –Analyse.

•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)

•40159 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 ID Participant name Participant city Participant country

12 John Doe Duckstad Federal Feather

Element ID Element name Description Units

7 TX2 Maximum temperature, recorded at 12 GMT 0.1 ºC

Station ID Station name Latitude Longitude Height Details

312 Fort Bourke +54.12 -32.46 103 Soil, high trees at SW 150 meters

Series ID Participant ID Element ID Station ID Public

955 12 7 312 Y

Series ID Series date Series value

Series Quality Control Flag

955 1981-03-01

254 0

... ... ... ...

955 2000-12-04

-197 1

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

•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=0 t=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

95411001981-01-0543

0626002031981-01-0443

95502541981-01-0343

Series IDSeries QC flagSeries valueSeries dateLocationID

............43

0626101232005-03-2143

‘core’ ECA-series

Nearby Synop-series

Nearby ECA-series

Nearby Synop-series

Continuous, up-to-date series

t=0 t=now

Series ID Participant ID Element ID Station ID Public

955 12 7 312 Y

43103-32.46+54.12Fort 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

http://eca.knmi.nl

Email at [email protected]