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Observatory data: Quality and use in repeat station data reduction Mioara Mandea (1), Monika Korte (1), Nils Olsen (2) (1) GeoForschungsZentrum Potsdam,

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Presentation on theme: "Observatory data: Quality and use in repeat station data reduction Mioara Mandea (1), Monika Korte (1), Nils Olsen (2) (1) GeoForschungsZentrum Potsdam,"— Presentation transcript:

1 Observatory data: Quality and use in repeat station data reduction Mioara Mandea (1), Monika Korte (1), Nils Olsen (2) (1) GeoForschungsZentrum Potsdam, Germany (2) Danish National Space Center, Copenhagen, Denmark With thanks to H.-J. Linthe, J. Haseloff, J. Schulz, K. Tornow, A. Glodeck, I. Matthes, M. Krüger

2 Importance of observatory data for repeat stations Observatory variation recordings necessary for repeat station data reduction Observatory annual means are based on hourly means Observatory annual means are used for repeat station data reduction to “annual means”  Errors in observatory data can map directly into repeat station “annual means” C(x i,t mean ) = C(x i,t i ) – C(O,t i ) + C(O, t mean ) Repeat station “annual mean” of component C Observatory annual mean of component C Repeat station measurement value at time t i Observatory recording at time t i

3 Aim: A high-quality set of global observatory hourly mean values for scientific studies, 1 st step: 1995 to current (2003) Data base: hourly means data base of WDC C1 for Geomagnetism, Copenhagen (now at WDC Edinburgh) Problem: Data in the data base may contain errors - outliers - unreported base line jumps - uncorrected base line drifts Strategy: - check all data for problems - compile a list of detected problems - encourage individual observatories to correct their data This project: quality check of global observatory annual means Overview over missing data Mean daily variation per year and observatory Comparison to CM4 model Intercomparison of observatories check all data for problems

4 Intercomparison of observatories NIEMEGK (NGK)CHAMBON LA FORET (CLF)

5 Intercomparison of observatories OBS 1 OBS 2 Difference X (nT) Y(nT) Z(nT) Differences due to inhomogeneous external and induced fields depending on distance between observatories and magnetic activity Only slow variations due to secular variation (~linear) No sudden jumps, no strong slow variations on sub-annual scale (baseline!)

6 Intercomparison of observatories LANZOU (LZH) GOLUMD (GLM)

7 Intercomparison of observatories OBS 1 OBS 2 Difference X (nT) Y(nT) Z(nT) Jump Jump ?

8 Intercomparison of observatories PANAGJURISHTE (PAG) ISTANBUL (ISK) SURLARI (SUR)

9 Intercomparison of observatories OBS 1 OBS 2 Difference X (nT) Y(nT) Z(nT) Baseline Drift Jump ? Technical disturbance ? Outlier ?

10 Intercomparison of observatories OBS 1 OBS 2 Difference X (nT) Y(nT) Z(nT) Baseline Drift Jump ? Technical disturbance ? Outlier ? Problems in ISK Problems in SUA

11 Relevance for repeat station data C(x i,t mean ) = C(x i,t i ) – C(O,t i ) + C(O, t mean ) Repeat station “annual mean” of component C Observatory annual mean of component C Repeat station measurement value at time t i Observatory recording at time t i 1. Jumps within the year: - annual means incorrect (if jump not corrected) - C(O,t i ) and annual means might be at different levels (if jump corrected for annual mean, but not in original variation data) 2. Base line drifts: - annual mean incorrect - reduction from a certain time of measurement to annual mean incorrect

12 ZONE 2 OBS Comparison 1Comparison 2 YearDays(about) Components Problem Obs1Obs2 Obs1Obs2XYZ LZHGLMLZH CDP Ja Neinclear jump GLM LZMGLMCDP JaNein jumps THJCDPTHJ Nein Jaspike BMTQIXBMT SSH Ja Neinjump BMTQIXBMT SSH Nein Jajumps SSHWHNSSH Nein Jaspike SSHWHNSSH JaNein spike PHU THJPHULNP , 310NeinJaNeinjumps PHU THJPHUTHJ ,18NeinJaNeinjumps THJPHUTHJPHUTHJ JaNein spike PHU LNPTHJPHU Ja jumps PHU LNPTHJPHU NeinJaNeinspike PHU CDPPHUTHJ JaNein jump THJ PHU CDP JaNein spike PHUTHJPHU CDP JaNein jumps, drift LNP GZHLNPKNY Ja drift LNP GZHLNPKNY NeinJaNeindrift SIL THJSILCDP NeinJaNeinBl problem SIL THJSILCDP Nein Jajumps, drift Tables of identified problems

13 Comparison to CM4 model CM4: quiet time model modulated with indices Dst and F10.7 solar flux (Sabaka et al., 2004)

14 Comparison to CM4 model - Differences CM4: quiet time model modulated with Dst and F10.7 solar flux indices (Sabaka et al., 2004) Base line problem?

15 High latitudes Problem: Strong external variations differ a lot from year to year differ a lot between observatories are not sufficiently described by CM4

16 Conclusions Most of the geomagnetic hourly mean values stored at the WDC C1 are of good quality but some erroneous data exist in the data base. Comparisons of mean daily variations per year, intercomparisons between neighbouring observatories and comparisons to global models are useful methods to detect different kinds of problems. For high geomagnetic latitudes and very remote observatories the detection of erroneous data is very challenging and not possible to the same degree of accuracy as for closely adjacent observatories at lower latitudes. Tables and plots of identified problems are available on a website. under link Projects: Global quality check of observatory data However, we generally cannot explain the sources of errors. We strongly encourage each observatory with some erroneous data to make every effort to understand the origin of the problems, correct their data and re-submit improved data to the WDC. Repeat station data quality also depends on observatory data quality


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