1 / 13

Total Ozone Data Base for COST - 726 MCM 7: Stockholm, September 11-12, 200 6

Total Ozone Data Base for COST - 726 MCM 7: Stockholm, September 11-12, 200 6. Janusz Krzyścin. Institute of Geophysics Polish Academy of Sciences Warsaw, Poland. Total Ozone Input Model of reconstruction of daily total ozone Examples of the reconstructed time series

afric
Télécharger la présentation

Total Ozone Data Base for COST - 726 MCM 7: Stockholm, September 11-12, 200 6

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Total Ozone Data Base forCOST-726MCM7: Stockholm, September 11-12, 2006 Janusz Krzyścin Institute of Geophysics Polish Academy of Sciences Warsaw,Poland

  2. Total Ozone Input • Model of reconstruction of daily total ozone • Examples of the reconstructed time series • Structure of the COST-726 Ozone Data Base

  3. Total Ozone Input National Institute of Water and Atmosphere Research (NIWA), Lauder, New Zeland, Assimilated Total Ozone Data Base 1979-2004 (Bodeker et al., 2005) Version 8 Nimbus 7 and Earth Probe TOMS GOME version 3.1 KNMI TOGOMI Version 8 SBUV from NIMBUS 7, NOAA9, NOAA 11, and NOAA 16 satellite Dobson Stations (30°-60°N) Bias < 1% ± 2-3%

  4. Dobson-NIWA Comparison

  5. Total Ozone Reconstruction Two Steps Regression Model 1- Ozone(month)= Regression_A(Indices of Global Atm. Circulation, Monthly Mean Meteo. Variables) 2. Ozone (day) - Ozone(month) = Regresion_B( Daily Meteo Variables– Monthly Mean Meteo Variables) Training period 1979-2004: Ozone(month), Ozone(day) from NIWA Data Base Model Ozone = Regression_A + Regression_B Model Total Ozone since beginning of proxy data -1950 !

  6. Dobson-NIWA Comparison

  7. Dobson- Reconstructed Ozone Comparison

  8. Dobson- Reconstructed Ozone Comparison

  9. Dobson- Reconstructed Ozone Comparison

  10. Dobson- Reconstructed Ozone Comparison

  11. * Model Skill *X(Model)= (Dobson-Model)/ModelSkill Coeff(Model) = Var (X(Simple))/Var(X(Model)) • SKILL COEFFICIENT t<1979 (EARLY DOBSON) • COST ERA-40 • S E A S O N • 5-9 10-4 5-9 10-4 AROSA 2.0 2.6 1.2 1.1 OXFORD 1.9 2.7 2.1 2.3 LERWICK 1.6 1.9 1.9 1.1 UPPSALA 2.0 1.9 1.9 2.1 SIMPLE MODEL

  12. General Comments Performance of the COST model (warm period) for t <1979 long-term bias ± 2%, standard dev. 3%-7% (larger standard errors probably due to uncertainties of the Dobson measurements in cloudy conditions, see Lerwick) COST reconstructed ozone almost equivalent ERA-40 Ozone usually bias smaller for COST but smaller standard dev for ERA-40 COST and ERA-40 model better than simplified model using constant daily ozone values derived from NIWA data COST model = quality control tool for the Dobson ozone

  13. COST-726 Total Ozone Data Base Area and Resolution (30°-70°N, 15°W-30°E) - area as defined by MCM6 (1°.0 (latitude)×1°.25(longitude) – as in NIWA Base) Start and End date: Jan 1950 - Dec 1978 : COST model Jan 1979 - Dec 2004 : NIWA +COST/ERA (if no NIWA) Jan 2005 - Dec ???? :OMI on AURA spacecraft Time Table to be completed before next MCM Meeting (December 2006)

More Related