operational quality control in helsinki testbed n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Operational Quality Control in Helsinki Testbed PowerPoint Presentation
Download Presentation
Operational Quality Control in Helsinki Testbed

Loading in 2 Seconds...

play fullscreen
1 / 17

Operational Quality Control in Helsinki Testbed - PowerPoint PPT Presentation


  • 129 Views
  • Uploaded on

Operational Quality Control in Helsinki Testbed. Mesoscale Atmospheric Network Workshop University of Helsinki, 13 February 2007 Hannu Lahtela & Heikki Turtiainen. What is Quality?.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Operational Quality Control in Helsinki Testbed' - sebastian


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
operational quality control in helsinki testbed

Operational Quality Control in Helsinki Testbed

Mesoscale Atmospheric Network Workshop

University of Helsinki, 13 February 2007

Hannu Lahtela & Heikki Turtiainen

what is quality
What is Quality?
  • “The degree to which a system, component, or process meets (1) specified requirements, and (2) customer or users needs or expectations” – IEEE
  • ”Data are of good quality when they satisfy stated and implied needs... [such as] required accuracy, resolution and representativeness.” – WMO Guide to Meteorological Instruments and Methods of Observation
quality management and quality control qc
Quality Management and Quality Control (QC)
  • “The purpose of quality management is to ensure that data meet requirements (for uncertainty, resolution, continuity, homogeneity, representativeness, timeliness, format, etc.) for the intended application, at a minimum practicable cost. Good data are not necessarily excellent, but it is essential that their quality is known and demonstrable.“
  • “Quality control is the best known component of quality management systems, and it is the irreducible minimum of any system. It consists of examination of data at stations and at data centres to detect errors so that the data may be either corrected or deleted *.”
  • WMO Guide to Meteorological Instruments and Methods of Observation
  • *) ”Deleted” must be understood here in the sense that erroneous data is not used for applications – however, it should remain stored in the database, only flagged faulty.
other quality management functions
Other Quality Management functions
  • In addition to QC, Quality Management includes:
    • equipment specification and selection
    • station siting and sensor exposure planning
    • maintenance & calibration procedures
    • data acquisition and processing (sampling, averaging, filtering...)
    • personnel training and education
    • metadata management
    • etc...
quality flags
Quality Flags
  • Information about suspicious or certainly wrong data values detected in the QC process should be passed on together with an information label, or flag, in order to:
  • • indicate the quality level
  • • inform which control methods and control levels data have passed
  • • inform about the error type if an error or suspicious value was found
  • Such flagging information is useful both in quality control phases (technical flags) and for users of meteorological information (end-user flags).
htb uses fmi end user flagging system
HTB uses FMI end-user flagging system
  • Four-digit code, one digit for each QC level:
  • HQC QC2 QC1 QC0
  • 1000 100 10 1
  • Value of the digit defines quality of the data:
  • 0 no check 4 calculated
  • 1 OK 5 interpolated (spatial)
  • 2 suspicious, small difference 8 missing
  • 3 suspicious, large difference 9 deleted
  • Example: 1531
  • 1 = QC0 at the site is OK
  • 30 = QC1 found big difference (e.g. monthly limit exceeded)
  • 500 = QC2 interpolated the value using neighbour station data
  • 1000 = HQ accepted the interpolated value
slide8

Proposal for HTB QC process (by Jani Poutiainen) - so far implemented only partially and with some modifications.

slide9

Proposal for HTB QC process (by Jani Poutiainen) - so far implemented only partially and with some modifications.

metman qc1 quality control
Metman – QC1 Quality Control
  • Quality control of weather observations is based on real time quality control, containing the following quality control tests:
    • range test
    • step tests ( 1hr and 3 hrs)
    • persistence test
    • spatial test
  • At present the following observations are tested:
    • wind speed (10 min. average)
    • barometric pressure
    • air temperature
  • The best fit quality control algorithms and recommendations by NORDKLIM (KLIMA report no 8/2002) and Oklahoma Mesonet QC are superimposed on the Metman quality control process.
metman qc control domains
Metman QC: Control Domains

Each weather stations must be part of quality control domain. Each quality control domain contains predetermined suspicious and erroneous limits for each parameters needed in each test. The values can be configured based on seasonal climate extremes.

Also meteorologically non-representative and representative weather stations should be located in different quality control domains.

Spatial test can be performed only with stations located on same representative quality control domain.

In Helsinki Testbed project all weather stations belong to one and the same quality control domain.

However, there are some special sites that should belong to a different domain. For example air temperatures in Heimoonkruoppi differ dramatically from the weather stations near by.

metman qc1 process
Metman QC1 process

QC1-process

Range test

data flow

without

qc-flag

erroneous

data flow

with

qc-flags

valid

Suspicious

Step tests

erroneous

valid

Suspicious

Persistence test

valid or erroneous

Suspicious

Spatial test

(under testing)

valid, suspicious or erroneous

Technical flag code is stored in the MetMan database. Four-digit end-user flag code is composed, converted to FMML and posted to CDW together with the observation data.

metman qc range test
Metman QC: Range test
  • Range test is a test that determines if an observation lies between predetermined range.
  • Erroneous ranges are based on sensor specifications and suspicious ranges can be configured based on seasonal climate extremes.
  • Metman real time quality control process performs range test first.
  • Range test doesn't need historical observations to perform.
  • If range test:
  • succeeds, step test will be performed next
  • fails, the rest of the tests won't be performed, and observation is flagged with erroneous flag
  • gets suspisious value, spatial test will be performed
metman qc step tests
Metman QC: Step tests
  • Step tests use sequential observations (1-hour and 3-hours) to determine which data represent unrealistic 'jumps' during the observation time interval.
  • Erroneous and suspicious step thresholds can be configured based on seasonal climate extremes.
  • Metman real time QC process performs step tests after the range test.
  • Step tests need historical observations to perform.
  • If the tests:
  • succeed, persistence test will be performed next
  • fail, the rest of the tests won't be performed, and observation is flagged with erroneous flag
  • get suspicious value, spatial test will be performed
metman qc persistence test
Metman QC: Persistence test
  • Persistence test analyses data on hourly basis to determine if observation underwent little or no variation.
  • Metman real time quality control process persistence test after the step test.
  • Persistence test needs historical observations to perform.
  • If the test:
  • succeeds, observation is flagged with valid flag
  • fails, observation is flagged with erroneous flag
  • gets suspicious value, spatial test will be performed
metman qc spatial test
Metman QC: Spatial test
  • Spatial test performs intercomparison between neighbour stations in the same quality control domain.
  • Metman real time quality control processes spatial test only if one of the earlier tests returns suspicious value.
  • Spatial test searches a nearby reference station and compares the parameter under test with that of the reference station. The reference station must
    • belong to the same QC domain
    • be sufficiently close
    • have about the same altitude and installation heights
    • the reference parameter must have passed range-, step- and persistence tests.
  • The spatial test is currently under testing, not yet operational.
htb qc next steps
HTB QC: next steps
  • Extension of QC1 to all measured parameters
  • Implementation of spatial test
  • Availability of end-user flags through Researcher’s Interface
  • Addition of technical flagging to CDW?
  • Special challenge for dense mesoscale networks:

Large number of stations => maintenance based on immediate response too expensive => new methods and tools needed for QC, network diagnostics and maintenance!