Transcript Automated Operational Validation of Meteorological Observations in the Netherlands Wiel Wauben, KNMI, The Netherlands     Introduction QA/QC chain Measurement system and users Status.

Automated Operational Validation of
Meteorological Observations in the
Netherlands
Wiel Wauben,
KNMI, The Netherlands
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Introduction
QA/QC chain
Measurement system and users
Status
Introduction
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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.
QC chain
MetNet
maintenance
Off-line
On-line
External
Site
surveys
&
inspection
6 months,
technical
& station
Pre- and
post
calibration
Calibration
period 8-24
months or
problems,
allowed
range for
deviation
Sensor
validation
Range, jump
persistency,
basic interrelation
Instrument
selection
Procedures
Station
validation
MetNet
spatial
validation
Interrelations,
temporal,
spatial
Export
manual
validation
Off-line, daily
Reporting vs
sensor
errors,
Handling of
quality
information
Real-time?
User
reports
ECMWF
HIRLAM
black lists
Data flow (MetNet)
APL
application
suite
Lightning
& radar
Sensor
1’5’
Sensor
SIAM
ADCM
airport
airbase
Sensor
12”10’
Station
12”  30’
Platforms
RMI
CIBIL
central
system
Aviation
MSS
message
switch
KMDS
OMWA
real-time
database
International
1h1d
VIVID
extraction
FTP
server
Internal
1’10’
Climatological
database
Intranet
applications
National
10’1d
External
clients
Basic assumptions
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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
Follow up
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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
Ceilometer (NI, QG and statistics)
Ceilometer statistics
Radar versus precipitation gauges
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Scatter plot
Daily sums
Dependent
verification
since bias is
removed
VIMOLA vert. integr. LAM
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Quasi geostrofic
P at msl
10m wind
currently short
term forcast
using hourly
data
“any” resolution
indicates
suspect P
values
Current valiation (daily, non-RT)
Outlook
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Make business case for basic 10-min near
real-time validation
Investigate other possibilities for temporal,
spatial and interrelations in RTV
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QC at other NMI’s
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Start implementation of basic version
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Allow for extensions/generalisation