Transcom, Paris 13 June 2005 Estimating Atmospheric CO 2 using AIRS Observations in the ECMWF Data...

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


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


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)


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


Channel selection


Error estimates

12 2 T 1a b

H R H12 2 T 1

a b H R H

22

ji

jir

22

ji

jir


Assimilation Error

a

N1

N

aiN

1

aj

N

aiijr :ncorrelatio Extra


Results


Comparison with JAL

Flight data kindly provided by H. Matsueda, MRI/JMA


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


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


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


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


Comparison with LMDz

ECMWF estimates LSCE CO2 simulation


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.


Radon simulation

12 hour

Forecast

Analysis

Radon

Analysis

Radon

12 hour

Forecast


Radon experiments

Transcom, Paris 13 June 2005 Estimating Atmospheric CO 2 using AIRS Observations in the ECMWF Data...

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