![Page 1: Transcom, Paris 13 June 2005 Estimating Atmospheric CO 2 using AIRS Observations in the ECMWF Data Assimilation System Richard Engelen European Centre](https://reader030.vdocuments.site/reader030/viewer/2022032704/56649d545503460f94a304dd/html5/thumbnails/1.jpg)
Transcom, Paris 13 June 2005
Estimating Atmospheric CO2 using AIRS Observations in the ECMWF
Data Assimilation System
Richard Engelen European Centre for Medium-Range Weather Forecasts
Thanks to Yogesh Tiwari and Frédéric Chevallier for model comparison plots
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Transcom, Paris 13 June 2005
Outline
• Why estimate CO2 at a NWP centre?
• Current setup of CO2 data assimilation system
• Error estimation
• Monthly mean results
• Comparisons with independent observations
• Comparisons with CO2 models
• Outlook
• Radon experiments
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Transcom, Paris 13 June 2005
Why at a NWP centre?
Advantages:
• Strong constraint on temperature and water vapour from all sorts of conventional and satellite observations, which allows focus on extraction of CO2 information from AIRS
• Experience with handling, processing, and assimilation of large amounts of data
• Good observation monitoring capability
Disadvantage:
• Time scale conflicts between medium-range weather forecast and environment monitoring (e.g., bias correction, tracer transport modelling)
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Transcom, Paris 13 June 2005
Description of current CO2 assimilation system
• CO2 is currently treated as a so-called ‘column’ variable within the 4D-Var data assimilation system.
• This means that CO2 is not a model variable and is therefore not moved around by the model transport.
• For each AIRS observation location a CO2 variable is added to the control (minimisation) vector. The CO2 estimates therefore make full use of the 4D-Var fields of temperature, specific humidity and ozone.
• The CO2 variable itself is limited to a column-averaged tropospheric mixing ratio with fixed profile shape, but a variable tropopause.
• A background of 376 ppmv is used with a background error of 30 ppmv.
• 18 channels in the long-wave CO2 band are used
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Transcom, Paris 13 June 2005
Channel selection
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Transcom, Paris 13 June 2005
Error estimates
12 2 T 1a b
H R H12 2 T 1
a b H R H
22
ji
jir
22
ji
jir
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Transcom, Paris 13 June 2005
Assimilation Error
a
N1
N
aiN
1
aj
N
aiijr :ncorrelatio Extra
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Transcom, Paris 13 June 2005
Results
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Transcom, Paris 13 June 2005
Comparison with JAL
Flight data kindly provided by H. Matsueda, MRI/JMA
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Transcom, Paris 13 June 2005
Comparison with JAL
Flight data kindly provided by H. Matsueda, MRI/JMA
St.dev. = 1.3 ppmv and RMS = 1.4 ppmv for 5-day mean on a 6˚ x 6˚ grid boxSt.dev. = 1.3 ppmv and RMS = 1.4 ppmv for 5-day mean on a 6˚ x 6˚ grid boxSt.dev. = 1.5 ppmv and RMS = 1.7 ppmv for 5-day mean on a 6˚ x 6˚ grid boxSt.dev. = 1.5 ppmv and RMS = 1.7 ppmv for 5-day mean on a 6˚ x 6˚ grid boxSt.dev. = 1.0 ppmv and RMS = 1.1 ppmv for 5-day mean on three 6˚ x 6˚ grid boxesSt.dev. = 1.0 ppmv and RMS = 1.1 ppmv for 5-day mean on three 6˚ x 6˚ grid boxes
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Transcom, Paris 13 June 2005
Comparison with CMDL
Flight data kindly provided by Pieter Tans, NOAA/CMDL
Molokai Island, Hawaii
Dots: CMDL flight observation; Black line: ECMWF estimate
Dotted line: Background value
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Transcom, Paris 13 June 2005
Comparison with CMDL
Flight data kindly provided by Pieter Tans, NOAA/CMDL
Scatter diagrams between mean flight profile concentrations and analysis estimates for various stations show good results.
St.dev.=1.6; RMS=1.6 St.dev.=0.7; RMS=1.1
St.dev.=1.0; RMS=1.6St.dev.=0.6; RMS=0.6
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Transcom, Paris 13 June 2005
TM3 LMDzJan - Feb
Mar - Apr
May - Jun
Jul - Aug
Sep - Oct
Nov - Dec
Solid = AIRS Dashed = Model
2 ppmv
AIRS compared with models for
2003
AIRS compared with models for
2003
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Transcom, Paris 13 June 2005
Comparison with LMDz
ECMWF estimates LSCE CO2 simulation
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Transcom, Paris 13 June 2005
Outlook
• Experimental work on CO2 data assimilation will evolve into a full greenhouse gas data assimilation system within GEMS project
• Other satellite observations will be assimilated:
IASI
CrIS
OCO
GOSAT
Main issue will be the definition of our background error covariance matrix. This represents the error in the model transport and the prescribed fluxes.
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Transcom, Paris 13 June 2005
Radon simulation
12 hour
Forecast
Analysis
Radon
Analysis
Radon
12 hour
Forecast
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Transcom, Paris 13 June 2005
Radon experiments
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Transcom, Paris 13 June 2005
Radon experiments
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Transcom, Paris 13 June 2005
Radon experiments
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Transcom, Paris 13 June 2005
Radon experiments
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Transcom, Paris 13 June 2005
Radon experiments
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Transcom, Paris 13 June 2005
Radon experiments