satellite data assimilation of atmospheric composition · pdf filebasic data assimilation...

Satellite data assimilation of atmospheric composition

Richard Engelen

Contributions from: Angela Benedetti, Abhishek Chatterjee, Dick Dee,

Steve English, Johannes Flemming, Antje Inness, Johannes Kaiser,

Sebastien Massart, Tony McNally, and Paul Poli

NWP-SAF Training Course Slide 1


Why atmospheric composition at an operational weather centre?

People are affected by air quality and ask for better information

Why this lecture?

Basic data assimilation theory is the same for atmospheric composition, but…

Radiance assimilation is not always feasible (yet)

Atmospheric composition data assimilation is much more determined by things like emissions and chemistry than by the initial values

With many species not being observed, the problem is even more underdetermined than the standard NWP case

Atmospheric composition impacts the basic NWP problem as well

Slide 3NWP-SAF Training Course

dzdz

dzTBL

0

)())(,()(

ATMOSPHERIC TEMPERATURE SOUNDING in NWP

If radiation is selected in a sounding channel for which

and we define a function K(z) =

dz

d

When the primary absorber is a well mixed gas (e.g. oxygen or CO2) with

known concentration it can be seen that the measured radiance is

essentially a weighted average of the atmospheric temperature profile,

or

dzzKzTBL

0

)())(,()(

The function K(z) that defines this vertical average is known as a WEIGHTING FUNCTION


Not well-mixed absorbers

ATMOSPHERIC TEMPERATURE SOUNDING in NWP

and we define a function K(z) =

dz

d

When the primary absorber is a well mixed gas (e.g. oxygen or CO2) with

known concentration it can be seen that the measured radiance is

essentially a weighted average of the atmospheric temperature profile,

or

dzzKzTBL

0

)())(,()(

The function K(z) that defines this vertical average is known as a WEIGHTING FUNCTION

We assume K(z) is constant, but it varies in

space and time introducing a modelling error

in the radiative transfer.

Variational bias correction: The modified analysis problem

Jb: background constraint

Jo: observation constraint

h(x)yRh(x)yx)(xBx)(x(x)1T

b

1T

b J

The original problem:

h(x)β)(x,byRh(x)β)(x,by

β)(βBβ)(βx)(xBx)(xβ)J(x,

o

1T

o

b

1

β

T

bb

1

x

T

b

Jb: background constraint for x J: background constraint for

Jo: bias-corrected observation constraint

The modified problem:

Bias (β)

Use realistic CO2 in radiance assimilation

Using modelled CO2 in AIRS/IASI radiance

assimilation leads to significant reduction in

needed bias correction.

Small positive effect on T analysis and neutral

scores.

Reduced

AMSU-A

Bias

Correction

Reduced AIRS and IASI Bias Correction

Fixed

CO2

Variable

CO2

Many trace gases can be measured in the UV-VIS, infrared, and microwave parts of the spectrum.

There is a wealth of information available in the observed radiances.


The A-train

European stage


Sentinel-5p

Sentinel-4Sentinel-5

IASI & GOME-2

SO2, GOME-2, SACS,

BIRA/DLR/EUMETSAT

NO2, OMI, KNMI/NASAAerosol Optical Depth, MODIS, NASA

Some examples O3, OMI, KNMI/NASA

Atmospheric composition observations traditionally come from UV/VIS measurements. This limits the coverage to day-time only. Infrared/microwave are now adding more and more to this spectrum of observations (MOPITT, AIRS, IASI, MLS, MIPAS …)


Challenges for Atmospheric Composition

Many species are observed in UV-VIS part of the spectrum, which is difficult to model

Quality of NWP depends predominantly on initial state - AC modelling depends on initial state (lifetime) and surface fluxes

CTMs have larger biases than NWP models (fluxes, chemistry, aerosol processes)

Most processes take place in boundary layer, which is not well observed from space. Dependence on solar radiation limits temporal sampling.

Only a few species (out of 100+) can be observed

NWP-SAF Training Course

RADIANCES VS. RETRIEVALS


Use of retrievals in NWP – the 80s


Kelly and Pailleux, 1988

Assimilating temperature and water vapour satellite retrievals caused severe problems. Only after switch to radiance assimilation the real value of satellites was seen.

“The SATEMs contain some good information, but also a lot of noise and a lot of bad data. The analysis manages to maintain a large part of the good information, but is also affected by the poor quality data.”

Credit: S. English

No satellite data

L2 satellite data

So what was the problem?


T 1 T 11 1( ) ( ) ( ) [ ( )] [ ( )]

2 2

b b o o

r r rJ H H x x x B x x y x R y x

L2 retrievals generally use same methodology as data assimilation. Minimize a Cost function that contains the observations and some a priori constraint:

The retrieved value will be biased relative to the assimilation model background, when the prior information is different from the model background.

This bias will have a vertical structure based on the vertical sensitivity of the observations.

Simplified solution: b

r r x x x

1

1 1

1

T

r

T

r

S K R K B

A S K R K

How do we try to use L2 retrievals in 2015?

Slide 17

( ) ( )b b b

r r r r x x A x x Ax I A x

The averaging kernel A describes the vertical structure of the impact of the a priori information.

R: observation error covariance matrixB: prior error covariance matrixK: weighting function

Retrieval xr can be written (after linearization) as:

With a-priori xrb , error covariance matrix Sr and averaging kernel A:


Example MOPITT CO Averaging Kernels

Slide 18

From: Deeter et

al. (2003) JGR


• Diurnal variations of Tsurf affect retrieval over land.• CO near surface more detectable during day, AKs shift downwards • Diurnal variability of AKs largest over e.g. deserts, smallest over sea• If AKs are not used this can introduce an artificial diurnal CO cycle in the analysis

day night

Assimilating retrievals: Column retrieval example

Slide 19

We can make use of the averaging kernel A in the observation operator by using the following:

Note that some a-priori error assumptions are still in there and we assume everything is linear within the bounds of the a-priori assumptions. (And we still need to know xb and A in the observation operator calculations).


b b

r ry x x Ax Ax

mod mody AWx

where W is the interpolation operator from model levels to averaging kernel levels.

Assimilating retrievals: summary


• Retrieval teams can focus their expertise fully on specific observation

• Good communication between data providers and data assimilation users needed

• Good characterization of retrieval is crucial• Averaging kernels• A priori• Error estimates• Quality flags

INITIAL VS. BOUNDARY VALUES


4D-Var


NWP 4D-Var is mostly defined as an initial value problem. Only initial conditions are changed and model error is relatively small.

CO2 as an example


Boundary condition problem – CO2


For atmospheric composition, the boundary conditions are very important (surface fluxes, emissions,…).

Background error statistics can to some extent account for increased uncertaintyNMC

ENS

ENS + fluxes pert.

• NMC and ensemble method give similar statistics.• Ensemble method + perturbed fluxes give different statistics and geographical

differences


Impact of background errors


The background errors that also account for surface flux errors have much broader spatial features.

Assimilating GOSAT CO2 retrievals with the new background errors provides a much better fit to independent observations.

Another example: volcanic eruptions


Both initial conditions and emissions are important to get it right

Current research developments


• Use (data constrained) models for surface fluxes• Use of CTESSEL land carbon model in ECMWF/MACC

CO2 model

• Add flux increments to the assimilation control vector• Already tested in LETKF by Kang et al. with promising

results

• Use satellite observed flux estimates in DA system • Use of GFAS fire detection method at ECMWF

NO2: problems with fast chemistry


Credit: BIRA/IASB

NO2 data assimilation

Satellite observations of NO2 are not straightforward to assimilate.

Fast chemistry makes it difficult to treat it as an initial value problem without a proper chemistry adjoint, because of the strong diurnal cycle.

Credits: J-C Lambert (BIRA)

2

3 2 2 3

sunlightNO NO O

NO CH O NO CH O

NO2 data assimilation

Credits: J-C Lambert (BIRA)

12-hour 4D-Var window

Rapid chemical conversion within the 12-hour 4D-Var window means we cannot link an NO2 observation at the end of the window correctly to the initial state without a full chemical adjoint.

Partial solution through simple approximation of main chemical reaction

Short lived memory of NO2 assimilation

OMI NO2 analysis increment [%] Differences between

[1015 molec/cm2]

JF 2008

JJA 2008

JF 2008

JJA 2008

• Large positive increments from OMI NO2 assim• Large differences between analyses of ASSIM and CTRL• Impact is lost during subsequent 12h forecast• It might be more beneficial to adjust emissions (instead of IC)

12h fc from ASSIM and CTRLAnalysis and CTRL

SAMPLING ISSUES


Issues with Observations

Little or no vertical information from satellite observations. Total or partial columns retrieved from radiation measurements. Weak or no signal from boundary layer.

Fixed overpass times and daylight conditions only (UV-VIS) -> no daily maximum/cycle

Global coverage in a few days (LEO); often limited to cloud free conditions; fixed overpass time.

Retrieval errors can be large; small scales not resolved


OMISBUV/2 NOAA-17

SBUV/2 NOAA-18 MLS

SCIAMOPITT IASI

GOME-2OMI

Ozone

CO

NO2 GOME-2OMI SO2SCIA

NRT data coverage for reactive gases


Increment created by a single O3 obs

Increment from a single Total Column O3 observation

Profile data are important to obtain a good vertical analysis profiles

Horizontal

correlations

Standard

deviation

Vertical

correlations

Ozone background errors

Ozone observation of 247 DU, 66 DU lower than background


Limb-sounding ozone data assimilated in 2003 (MIPAS) and 2006-2008 (MLS)

These data, especially MLS, are clearly beneficial

OMI data are used from July 2007

No LS data

Importance of adequate observations


ONLY A FEW SPECIES ARE OBSERVED


Aerosol – prime example of ill-observed system


Example for wrong aerosol attribution

sulphate biomass

dust sea salt

Eruption of the Nabro volcano in 2011 put a lot of fine ash into the stratosphere.This was observed by AERONET stations and the MODIS instrument.

The MACC/ECMWF aerosol model does not contain stratospheric aerosol yet, so the observed AOD was wrongly attributed to the available aerosol types.

MACC AOD analysis

AERONET fine mode AOD

ICIPE-Mbita - AERONET

AERONET total AOD


Constraining aerosol


“The most comprehensive approach to monitoring intercontinental smoke transport is to use MISR to observe smoke injection height near source fires, OMPS to track plumes over long distances, MODIS to measure aerosol loading, and CALIOP to capture a vertical profiles of smoke plumes” - Hongbin Yu, University of Maryland.

Unconstrained chemistry


Only a small subset of all chemical species is observed from satellites. Most commonly routinely observed are O3, NO2, SO2, CO, HCHO, but some other species are available as well.

Adjoint of chemistry code is not straightforward and not commonly used at the moment. This means that the chemical balance can be disturbed by only changing a few species of the total set in the chemistry scheme.

Chemical species have a large range of scales


Sampling in summary

Poor sampling in space, time, and species creates problems

Large dependence on model (chemistry, fluxes, species)

Large dependence on correct background errors

Large variety of different satellite observations needed to better constrain the problem


Back to NWP

In 4D-Var, observations of atmospheric species can also tell us something about

the winds (see next slides).

Modelling aerosol (constrained by observations) improves the Earth radiation

budget, which is important for NWP.

Poor representation of aerosol Better representation of aerosol

Coupling between tracer and wind field in 4D-Var:illustration using 1D advection model

2

2

x

u

x

uu

t

u

0

x

qu

t

q

2

2

x

u

x

uu

x

uu

t

u

0

x

qu

x

qu

t

q

0

)(

x

qu

t

q

Tangent linear equations: Adjoint equations:

Model equations u = u(x,t) = wind over periodic domain [0,L]q = q(x,t) = passive tracer

= diffusion coef.= perturbations= adjoint variablesqu ,

qu ,

02

2

x

qq

x

uu

x

u

x

uu

t

u

Single observation experiments -Ozone and wind increments

4D-Var 12z 4D-Var 15z

4D-Var 9z3D-Var Level 20, 30 hPa

6h assimilation window

Observation at T0: 4D-Var = 3D-Var

Observation at T3: wind increments

Observation at T6: wind increments

What we have seen today…

Basic theory is the same

For mostly pragmatic reasons we have wound the clock back a bit and make more use of L2 retrievals

Important to correctly use the L2 information (AK, errors, a priori)

Atmospheric composition is much more a boundary value problem than an initial condition problem

Many species and aerosol size distributions make the system very much under-sampled

Progress is made by addressing all areas – still a lot of work to do

Atmospheric composition has the potential to improve various aspects of NWP


satellite data assimilation of atmospheric composition · pdf filebasic data assimilation...

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