Transcript ECMWF HPC requirments
Assimilation of Earth-observation data into Earth-system models Practical considerations in the transition from research to operations
Erik Andersson and Adrian Simmons European Centre for Medium-Range Weather Forecasts GlobMODEL workshop, ESRIN, 12-13 Sept 2007 Slide 1
What do we understand by Research?
Research into the use of Earth-Observation data in Earth System models can include work on:
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Use of new data in assimilation systems
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Data assimilation techniques
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Assimilating models
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Retrieval algorithms
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Radiative transfer modelling
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Design of new instruments and missions
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… GlobMODEL workshop, ESRIN, 12-13 Sept 2007 Slide 2
What do we understand by Operations?
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Routine numerical weather prediction
– For ranges from hours to seasons ahead •
Routine environmental monitoring
– GEMS follow-on project envisages operational running of system in both near-real-time and with a 5- to 6-month delay •
Periodic reanalysis
– ERA-15, ERA-40, ERA-??, …
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Operational NWP already makes a very prominent use of EO data Number of satellite sources used at ECMWF No. of sources
30 25 20 55 50 45 40 35 15 10 5 0 1996 1997 1998 1999 2000 2001 2002
Year
2003 2004 2005
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2006
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2007 2008 2009 AEOLUS SMOS TRMM CHAMP/GRACE COSMIC METOP MTSAT rad MTSAT winds JASON GOES rad METEOSAT rad GMS winds GOES winds METEOSAT winds AQUA TERRA QSCAT ENVISAT ERS DMSP NOAA
Operational NWP already makes a very prominent use of EO data quantity of satellite data used per day at ECMWF
CONV+SAT WINDS TOTAL 24 22 20 18 16 14 12 10 8 6 4 2 0 1996 1997 1998 1999 2000
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2001 2002
Year
2003 2004 2005 2006 2007 2008
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2009
How has this been achieved?
• Through ECMWF dedicating staff to the use of satellite data • Through consultants at ECMWF funded by the Space Agencies (especially EUMETSAT and ESA) • Through cooperation among members of the European Meteorological Infrastructure – Eumetsat SAFs (NWP, GRAS, Ozone, OSI, …) – KNMI’s near-real-time supply of retrievals (SCIAMACHY, OMI) – … • Through cooperation with ESA on the monitoring and exploitation of data from specific missions • Through cooperation with weather prediction centres and space agencies outside Europe • Through general scientific interaction
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Implications of the already widespread use of Earth observation data
• Impact studies based on systems assimilating fewer types of observation may overestimate the impact of a new type of observation • Good bias correction (inter-calibration) is essential for benefit to be derived from a new type of observation • Transition from research to operations is most efficient if research is carried out close to operational system
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Near-real-time data supply and general preparedness for new missions
• Lack of near-real-time data supply is a barrier to the transition from research to operations for NWP – MIPAS – MLS – (and potentially, in the past, SMOS) • ADM good example of early preparedness
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Impact of MLS ozone profiles on ECMWF 4D Var ozone analyses (Elias Holm, R. Uni) GlobMODEL workshop, ESRIN, 12-13 Sept 2007 Slide 10
ILIAD - Impact of P, T and backscatter ratio on Rayleigh Responses of the ADM-Aeolus mission Line shape
Depends on P, T and s Mie Rayleigh (P,T) Response curve
Function of P, T and s Transmission
through the Double FP Light transmitted through T A and Dabas Meteo-France T B
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Response (function of f D )
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Emerging Earth-system models
• Earth system models are being developed for climate monitoring (and prediction) and for environmental monitoring / air quality – Atmosphere – Ocean – Land and sea surface – Biosphere – Cryosphere • Current available Earth-model systems are incomplete • Processes captured by the EO measurements may be missing or inadequately described by the model – hampering data/model fusion
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An Advanced Surface Analysis System
SMMR, SSM/I, TMI, AMSR, GRACE, AVHRR, MODIS, MERIS, ASCAT, ERS scat., SEVIRI, GOES Imager, ....
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The Current Surface Analysis System GlobMODEL workshop, ESRIN, 12-13 Sept 2007 Slide 14
Ongoing/recent research projects – surface analysis H-SAF
(EUMETSAT) -ERS scat (demo, re-analyses) -ASCAT (operations) (Scipal et al. 2007) ELDAS (EU) -MW TB (Seuffert et al. 2003, Seuffert et al. 2004)
SMOS DA I
(ESA) SMOS TB
SMASS
(DFG) - MW TB (Wilker et al. 2007)
NWP-SAF
(EUMETSAT) - SMOS TB (Holmes et al. 2007, Drusch et al. 2007)
Geoland I
(EU) - MODIS LAI (Jarlan et al. 2007)
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Expanding the contribution of the academic sector
Opening up the research systems of operational assimilation centres to much wider use by external research groups would: • Enable external research to be more relevant and readily applicable • Require support at both the operational and external research centres, as operational systems tend to be sophisticated and complicated, change over time, and are not necessarily designed to be friendly to outside users • Require long-term support, as support-team expertise needs to be built up, and lessons need to be carried over from research to near-real-time operations, and later to reanalysis • Train young researchers for future roles at operational centres
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Impact of MIPAS limb-radiance assimilation on zonal mean analyses (Niels Bormann, …, ASSET project) Humidity (%) Ozone (ppmv) GlobMODEL workshop, ESRIN, 12-13 Sept 2007 Slide 17
Impact of ESA/KNMI SCIAMACHY total column ozone on ECMWF 4D-Var ozone analyses (Rosana Dragani) GlobMODEL workshop, ESRIN, 12-13 Sept 2007 Slide 18
Conclusions – transition from research to operations
In many cases, interpretation of EO data is significantly enhanced through assimilation (fusion) into a comprehensive Earth system model, or components thereof • Research and development of Data assimilation techniques, Assimilating models, Retrieval algorithms, Radiative transfer modelling, … can be conducted in a distributed manner • New developments can be integrated into the operational system, after thorough testing and evaluation of its impact • (Components of) the operational system can be made available for experimentation in the research community – provided the required support teams can be established • The uptake of EO data can be accelerated by making the data available in (quasi) near-real time, as soon as possible after launch – later/reprocessed data are vital for reanalyses
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