Synthetizing, homogenizing, aggregating different ... · Synthetizing, homogenizing, aggregating...
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Synthetizing, homogenizing, aggregating different parameters observed at ground: the SIRTA-Re-OBS approach M. Chiriaco (1) , S. Cloché (2) , J. Badosa (2) , J.-C. Dupont (2) , M. Haeffelin (3) , J. Lopez (2) (1) LATMOS, (2) IPSL, (3) LMD [email protected] Context SIRTA-ReOBS • Important interannual and seasonnal variabilities • Important dispersion of the models • Low frequency variability and extremes badly represented by ensemble Source: Terray and Boé, CRG, 2013 Strong uncertainties at regional scale, which is an important scale in term of impacts At this scale, we must use observations to improve our understanding of the processes that create this variability Stations Météo-France 50km 15km Extraction sat. SIRTA-ReOBS : 12 years of multi-parameters observations at SIRTA Re = Re-calibration Re-quality control Re-averaging Re-treatment Re-expertise … Global annual temperature Temperature in France, in summer SIRTA Surface, 2m, vertical profiles Surrounding Meteo-France stations Extraction of satellite observations Harmonization treatment/inversion Synchronization and hourly averaging Quality control ++ Standardized nomenclature Evaluation of uncertainties and representativity Manage changing of instruments and algorithms Documentation/Metadata SIRTA observatory Classical meteorology More advanced measurements Parameters retrieved from an algorithm SIRTA-ReOBS : decadal synthesis of ≈50 measured parameters 2003 – 2014 for the oldest data Current content of SIRTA-ReOBS Related scientific publications sirta.ipsl.fr WCRP Climate Symposium 2014 Recommendation #12: “To enhance the value of existing observation records for climate research and applications, operational and research space agencies should put a sustained effort into re-processing and re- analyzing existing archived data to produce temporally homogeneous products to study climate variability and change” Since 1999: an atmospheric observatory in a peri- urban area, 20 km in the South-West of Paris 150 instruments: in situ, active and passive remote-sensing 5300 files/day 3Go of data /day 250 scientific publications Topics: climate, air quality, atmospheric processes, renewable energy… Challenge – have a dataset that : Involves different time-scale: from the decades to the diurnal cycle Is high quality in order to be able to detect low magnitude signals Allows multi-parameter studies Quality control for all variables: 0 = OK 1 = extreme value or incomplete hour 2 = out of the physical limits 3 = missing data Uncertainty for all variables: Instrumental resolution Each time-step, value of the temporal standard deviation during the current hour 1) Total dataset for downwelling radiative flux Day of the year Year Hour Next September: distribution of the dataset + a catalog of images like 1), via web-page and ftp 2) PDF and diurnal cycle T2 T2 RH RH DJF MAM JJA SON 3) Lidar profiles - histograms of : (1) SR (scattering ratio) and (2) STRAT classification Occurrence (log of %) from 2002 to 2014 SR bin STRAT classification (Morille et al. 2004) Badosa J., Chiriaco, M., et al.: SIRTA-reOBS: multi-parameter, long-term, homogenised, and all-in-1-file dataset of atmospheric observations at SIRTA supersite, in prep. Bastin S. et al. : Regional model evaluation using colocated long term ground based observations at SIRTA: when and why does WRF-MEDCORDEX simulation fails? Submitted to Climate Dynamics Cheruy F. et al. , 2013: Combined influence of atmospheric physics and soil hydrology on the simulated meteorology at the SIRTA atmospheric observatory. Climate Dynamics. Chiriaco M. et al. 2014: European heatwave in July 2006: Observations and modeling showing how local processes amplify conducive large‐scale conditions. Geophysical Research Letters. Chiriaco M.: Wich clouds are worming or cooling at SIRTA?. in prep. Campoy A., 2013: Response of land surface fluxes and precipitation to different soil bottom hydrological conditions in a general circulation model. Journal of Geophysical Research. Dione C.: Large-scale circulation influence on local processes for 3 different sites in France, in prep Pal S. et al. 2015: Dynamical features and forcing mechanisms governing diurnal and seasonal variability in the boundary layer depths: A five-year long lidar observations over a suburban site near Paris. Journal of Geophysical Research. One single NetCDF file , CF convention, updated every 6 months
Transcript of Synthetizing, homogenizing, aggregating different ... · Synthetizing, homogenizing, aggregating...
Synthetizing, homogenizing, aggregating different parameters observed at ground: the SIRTA-Re-OBS approach
M. Chiriaco(1), S. Cloché(2), J. Badosa(2), J.-C. Dupont(2), M. Haeffelin(3), J. Lopez(2) (1) LATMOS, (2) IPSL, (3) LMD
Context SIRTA-ReOBS
• Important interannual and seasonnal variabilities • Important dispersion of the models • Low frequency variability and extremes badly
represented by ensemble
Source: Terray and Boé, CRG, 2013
è Strong uncertainties at regional scale, which is an important scale in term of impacts
è At this scale, we must use observations to improve our
understanding of the processes that create this variability
Stations Météo-France 50km
15km Extraction sat.
SIRTA-ReOBS : 12 years of multi-parameters observations at SIRTA Re = Re-calibration
Re-quality control Re-averaging
Re-treatment Re-expertise …
Global annual temperature
Temperature in France, in summer
SIRTA Surface, 2m, vertical profiles
Surrounding Meteo-France stations
Extraction of satellite observations
Ø Harmonization treatment/inversion Ø Synchronization and hourly averaging Ø Quality control ++ Ø Standardized nomenclature Ø Evaluation of uncertainties and representativity Ø Manage changing of instruments and algorithms Ø Documentation/Metadata
SIRTA observatory
Classical meteorology
More advanced measurements
Parameters retrieved from an algorithm
SIRTA-ReOBS : decadal synthesis of ≈50 measured parameters 2003 – 2014 for the oldest data
Current content of SIRTA-ReOBS
Related scientific publications
sirta.ipsl.fr
WCRP Climate Symposium 2014 Recommendation #12:
“To enhance the value of existing observation records for climate research and applications,
operational and research space agencies should put a sustained effort into re-processing and re-analyzing existing archived data to produce
temporally homogeneous products to study climate variability and change”
Since 1999: an atmospheric observatory in a peri-urban area, 20 km in the South-West of Paris Ø 150 instruments: in situ, active and passive
remote-sensing Ø 5300 files/day Ø 3Go of data /day Ø 250 scientific publications
Topics: climate, air quality, atmospheric processes, renewable energy…
Challenge – have a dataset that : ² Involves different time-scale: from the decades to
the diurnal cycle ² Is high quality in order to be able to detect low
magnitude signals ² Allows multi-parameter studies
Quality control for all variables: § 0 = OK § 1 = extreme value or incomplete
hour § 2 = out of the physical limits § 3 = missing data
Uncertainty for all variables: § Instrumental resolution § Each time-step, value of the
temporal standard deviation during the current hour
1) Total dataset for downwelling radiative flux
Day of the year
Year
Hour
Next September: distribution of the dataset + a catalog of images like 1), via web-page and ftp
2) PDF and diurnal cycle T2
T2
RH
RH
DJFMAMJJASON
3) Lidar profiles - histograms of : (1) SR (scattering ratio) and (2) STRAT classification
Occurrence (log of %) from 2002
to 2014
SR bin
STRAT classification (Morille et al. 2004)
ü Badosa J., Chiriaco, M., et al.: SIRTA-reOBS: multi-parameter, long-term, homogenised, and all-in-1-file dataset of atmospheric observations at SIRTA supersite, in prep. ü Bastin S. et al. : Regional model evaluation using colocated long term ground based observations at SIRTA: when and why does WRF-MEDCORDEX simulation fails? Submitted to Climate
Dynamics ü Cheruy F. et al. , 2013: Combined influence of atmospheric physics and soil hydrology on the simulated meteorology at the SIRTA atmospheric observatory. Climate Dynamics. ü Chiriaco M. et al. 2014: European heatwave in July 2006: Observations and modeling showing how local processes amplify conducive large‐scale conditions. Geophysical Research Letters. ü Chiriaco M.: Wich clouds are worming or cooling at SIRTA?. in prep. ü Campoy A., 2013: Response of land surface fluxes and precipitation to different soil bottom hydrological conditions in a general circulation model. Journal of Geophysical Research. ü Dione C.: Large-scale circulation influence on local processes for 3 different sites in France, in prep ü Pal S. et al. 2015: Dynamical features and forcing mechanisms governing diurnal and seasonal variability in the boundary layer depths: A five-year long lidar observations over a suburban site
near Paris. Journal of Geophysical Research.
One single NetCDF file, CF convention, updated every 6 months
