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Developing long-term homogenized climate Data sets

Developing long-term homogenized climate Data sets. Olivier Mestre Météo-France Ecole Nationale de la Météorologie Université Paul Sabatier, Toulouse. The introduction you ever dreamed of…. « State of fear » (Michael Crichton). 1912 : Lescar primary school. 2007 : Pau-Uzein Airport.

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Developing long-term homogenized climate Data sets

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  1. Developing long-term homogenized climate Data sets Olivier Mestre Météo-France Ecole Nationale de la Météorologie Université Paul Sabatier, Toulouse

  2. The introduction you ever dreamed of…

  3. « State of fear » (Michael Crichton)

  4. 1912 : Lescar primary school 2007 : Pau-Uzein Airport Homogenisation : why?Example of Pau temperature series

  5. Pau: raw maximum temperatures (TX)

  6. Homogenisation : a very old problem! • « Comptes-rendus de l’Académie Royale des Sciences » - 1703

  7. Usual method: relative homogeneity PRINCIPLE : removing the climatic signal to put into evidence artificial shifts in the series Tested series minus COMPARISON series Reference series

  8. Shifts detection • Dynamic programming algorithm + penalized likelihood • Multiple comparisons of non-homogeneous series • Metadata!

  9. Shifts detection

  10. Correction • ANOVA model : correction of multiple non-homogenous series, provided change-point positions are well known. Climate factor µi + Station factor j4 j2 j5 j1 j3 + Noise

  11. Correction Climate signal estimation + Bias estimation in the station effects (monthly scale)  Correction+reconstitution of missing data Absolutely no assumption is made concerning the evolution of the climate signal

  12. « Before » « After » Correction of Pau maximum temperatures

  13. « Before » « After » Maximum temperatures : 1901-2000 trends

  14. Developments in Homogenisation • COST ACTION ES0601 : « Advances in HOmogenisation MEthods for climate series : an integrated approach » (HOME) http://www.homogenisation.org • Daily data homogenisation : study of extreme events

  15. Requirements in terms of data digitization • Fill the gaps and complete the target series as far as possible • Quality control and homogenisation techniques require complete neighbouring series : digitize every data, not only target series! • Metadata, station histories are as important as data itself Digitize metadata along with corresponding data!

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