automated operational validation of meteorological observations in the netherlands
DESCRIPTION
Automated Operational Validation of Meteorological Observations in the Netherlands. Wiel Wauben, KNMI, The Netherlands. Introduction QA/QC chain Measurement system and users Status. Introduction. Automated network for synop and climatological observations. - PowerPoint PPT PresentationTRANSCRIPT
Automated Operational Validation of Meteorological Observations in the
Netherlands
Wiel Wauben,KNMI, The Netherlands
Introduction QA/QC chain Measurement system and users Status
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.
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?
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’
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
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
Ceilometer (NI, QG and statistics)
Ceilometer statistics
Radar versus precipitation gauges
Scatter plot
Daily sums Dependen
t verification since bias is removed
VIMOLA vert. integr. LAM
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
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