Automated Operational Validation of Meteorological Observations in the Netherlands
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Automated Operational Validation of Meteorological Observations in the
Netherlands
Wiel Wauben,KNMI, The Netherlands
Introduction QA/QC chain Measurement system and users Status
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Introduction
Automated network for synop and climatological observations.
Data near real-time available to internal and external users every 10-minutes.
Observers at airports only for aeronautical reports, but 12 second wind and RVR data provided continuously.
Automated network requires automated validation in real-time.
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QC chain
Sensor
validation
Station
validation
MetNet spatial
validation
Export manual
validation
User
reports
ECMWF HIRLAM
black lists
Pre- and
post calibration
MetNet maintenance
Off-line
On-line
External
Site surveys
& inspection
6 months, technical & station
Calibration period 8-24 months or problems, allowed
range for deviation
Instrument selection
Procedures
Range, jump persistency, basic inter-
relation
Inter-relations, temporal,
spatial
Off-line, daily
Reporting vs sensor errors,
Handling of quality
information
Real-time?
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Data flow (MetNet)
ADCM airport airbase
CIBIL central system
KMDS OMWA
real-time database
VIVID
extraction
FTP
server
SIAM
Aviation
Climatological database
Sensor
Platforms RMI
Intranet applications
External clients
MSS
message switch
Lightning & radar
APL
application suite
Sensor 12”10’
Station 12” 30’
National 10’1d
International 1h1d
Internal 1’10’
Sensor 1’5’
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Basic assumptions
24*7 considered usefull and reduces manual labour
“No” delay in data flow QC does not change values Result of QC check in binary Q-flag Manual input (link to
technical/environmental changes) Alarm Validation results should be embedded in
QA/QC chain with suitable actions to eliminate causes
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Follow up
Overview current QC at various places Details of methodes and usefullness
(number, importance) Optimal location of QC (OMWA, 10min) Q indicators traceable throughout data
flow (sensor-interface BUFR report) Follow up (e.g. single jump in
temperature) User should use data AND quality (mask
applied for the users Start with MetNet but keep general
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Ceilometer (NI, QG and statistics)
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Ceilometer statistics
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Radar versus precipitation gauges
Scatter plot
Daily sums Dependen
t verification since bias is removed
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VIMOLA vert. integr. LAM
Quasi geostrofic
P at msl 10m wind currently
short term forcast using hourly data
“any” resolution
indicates suspect P values
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Current valiation (daily, non-RT)
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Outlook
Make business case for basic 10-min near real-time validation
Investigate other possibilities for temporal, spatial and interrelations in RTV
QC at other NMI’s Start implementation of basic version Allow for extensions/generalisation