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Automated Operational Validation of Meteorological Observations in the Netherlands Wiel Wauben, KNMI, The Netherlands Introduction QA/QC chain Measurement.

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Presentation on theme: "Automated Operational Validation of Meteorological Observations in the Netherlands Wiel Wauben, KNMI, The Netherlands Introduction QA/QC chain Measurement."— Presentation transcript:

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

2 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.

3 QC chain 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?

4 Data flow (MetNet)

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

6 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

7 Ceilometer (NI, QG and statistics)

8 Ceilometer statistics

9 Radar versus precipitation gauges Scatter plot Daily sums Dependent verification since bias is removed

10 VIMOLA vert. integr. LAM Quasi geostrofic P at msl 10m wind currently short term forcast using hourly data “any” resolution indicates suspect P values

11 Current valiation (daily, non-RT)

12 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


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