lecture 3 stratospheric chemistry data assimilation

Day 5 Lecture 3 Stratospheric Chemistry Data Assimilation Hendrik Elbern 1

DRAGON ADVANCED TRAINING COURSE IN ATMOSPHERE REMOTE SENSING

Lecture 3Stratospheric Chemistry

Data AssimilationH. Elbern

Rhenish Institute for Environmental Research at the University of Cologne

andVirt. Inst. for Inverse Modelling of Atmopheric Cjemical

Composition



Objectives of this lecture

Understand • different atmospheric chemistry applications

• required special treatments for the stratosphere

• the differences to meteorology

• some advanced examples



Main incentives of stratospheric chemistry data assimilation:

Make best use of atmospheric chemistry satellite data• Science:

– special challenge: heterogeneous polar chemistry• Weather forecasting: better calculation of the

radiative transfer equation (diabatic processes in the stratosphere)

• Stratospheric climate monitoring (trend detection)• Recently UV forecasts for exposion control of

human skin



Objective of stratospheric chemistry

A stratospheric chemistry data assimilation system should produce analysed fields of middle atmosphere chemical constituents, and

•make best use of all available (satellite) data, from heterogeneous sensors, scattered in space and time

•ensure chemical and dynamical consistency

•extend analysis on non-observed species (given sufficient coupling)

•grant numerical efficiency (grid design, parallelisation) for near realtime operation, if applicable



Assimilation with Chemistry-Transport Models• CTMs driven by off-line winds and temperatures, e.g.:

– Fisher and Lary 1995; – Khattatov et al. 1999; – Errera and Fonteyn 2001; – Stajner et al. 2001; – Eskes et al. 2003, – Marchand et al. 2004

• assimilation of ozone (profiles and total columns) now operational at a number of institutions making use of CTMs: – KNMI http://www.temis.nl/– BIRA-IASB http://www.bascoe.oma.be/– DLR-DFD http://taurus.caf.dlr.de– NASA http://gmao.gsfc.nasa.gov/operations/– DLR-DFD

http://auc.dfd.dlr.de/sensors/gome/products/data_products.html



Ozone Assimilation for global GCM-based NWP systems

• UK Met Office – Jackson and Saunders 2002; – Struthers et al. 2002; – Geer et al. 2004; – Lahoz et al. 2005

• European Centre for Medium-range Weather Forecasts, ECMWF – Dethof 2003– Dethof and Hólm 2004



Example: UARS MLS ozone data

UARS MLS ozone data at 10 hPa on 1st February 1997.

Ozone analyses at 10 hPa at 12 UTC on 1st February 1997.

Assimilating (UARS MLS) ozone and temperature data, plus operational data, into the Met Office assimilation system. Blue indicates low ozone values; red indicates high ozone values. See Struthers et al. (2002)



DARC/MetOffice analysis example of southern polar vortex split

Analysed ozone field at 12UTC on 23 September 2002. LHS plot: 450K; RHS plot:850 K. Dashed lines mark a great circle crossing the ozone holes. Units are ppmm. See Geer et al. (2004) for details.



ASSET data assimilation analysis comparison:Ozone (ppmm) at 68 hPa in the southern hemisphere on 31st August 2003, shown on a polar stereographic projection bounded by the equator.



TransportTransport--diffusiondiffusion--reactionreaction equationequation and and itsitsadjointadjoint



Adjoint integration Adjoint integration ““backward in timebackward in time””(see lecture 1)(see lecture 1)

How to make the parameters of resolvents i M(ti-1,ti) available in reverse order??



An example:

The operartional middle atmosphere data assimilation system for

Synoptic Analyses of Chemical constituents by Advanced Data Assimilation

SACADA



Features of the SACADA Assimilation System

GCM approach: German Weather Services global forecast model (GME) serves as an online meteorological driver to provide for consistent wind fields

Icosahedral grid, parallelisation and semi Lagrange transport scheme are adopted from GME

42 hybrid level ranging from the surface to 0.1 hPa (~65 km)

chemistry module

-Accounts for 148 gas phase and 7 heterogeneous reactions on aerosol and PSC surfaces

-2nd order Rosenbrock method to solve the system of stiff ODEswithout any family assumption



Parameterisation of the Background Error Covariance Matrix (BECM) using a diffusion approach (Weaver and Courtier, 2001)

Adjoint modules have been build for advection, gas phase chemistry and heterogeneous chemistry

Incremental formulation implemented

Features of the SACADA Assimilation System (2)



Selected DA system design:

• Data assimilation method: 4D variational– chemical consistency within assimilation

interval (and ensuing forecasts)– most flexibility for data types

• Numerical efficiency: icosahedral grid– nearly isotropic distribution of grid points– parallelisation with minor impact on code– straightforward grid refinement template



General Chemistry Circulation Model Structureonly 1x per

assimilation

n~20x(each iteration)



SACADA storage and recalculation strategysi

ngle

forw

ard

time

step

sing

le a

djoi

nt/b

ackw

ard

time

step



SACADA General Chemistry Circulation Modelwith GME icosahedral grid structure and met. model

grid:ni=32 ∆x~250 km(~T80, ~ 2.25°)transport: semi-Lagrange

L42: hybrid, surf 10 Pa∆zstrat~2 km

chemistry:41 specieshet. chem.:NAT, ICE, sulf,(Hendricks et al. 2002)grid cells: 12 pentagons all other: hexagons



Icosahedral Grid vs. Conventional lat/lon Grid

Ni = 32 distance between neighbouring grid points ~250 km, nearly homogeneous over the globe

10242 grid points per level

Corresponding to 2.25° mesh-size at the equator for lat/lon grid



Heterogeneous chemistry formulation(direct and adjoint)



Formulation of the background error covariance matrix:

Diffusion paradigm (Weaver and Courtier, 2001)

4D var needs the square root of the background error covariance matrix B (O=1012):Basic idea: 1. formulate covariances by Gaussians2. approximate Gaussians by integration of the diffusion operator over time T3. calculate B1/2 by integration over time T/2 (comp. cheap), and 4. intermittent normalisation (comp. more challenging)



B1/2 and BT/2 describing a quasi Gaussian correlationcan be modelled using a diffusion operator:



Analysis increment due to a single O3-observation

Correlation Length of 600 km assumed here

BECM is modelled using a diffusion approach

The increment in initial values is spread out to neighbouring grid-points depending on the correlations that are known / assumed.



Domain decomposition for 6 processors: Run time on an AMD Opteron,

INFINI band installation after 4 Iterations:

Diamond domain (2 adjacent triangles) decomposition for, shown here, 6 processors (colour coded): simple but effective strategy for load balancing (day—night)

Computational Aspects, Parallelisation Strategy



•Initial background was derived from SOCRATES model output (2D fields of chemical constituents)

•B-matrix was modelled using a diffusion approach:

-Background error 100% (first 6 days) / 50% (other days)

-Horizontal correlation between grid points quasi Gaussian with acorrelation length of 600 km (first 6 days) / 300 km (other days)

•R-matrix taken to be diagonal, errors from MIPAS-IMKdata

•Assimilation is done on 16 model levels (1.8 hPa – 88 hPa) resulting in about 5h wall-clock-runtime for 15 iterations

•Assimilation of MIPAS-IMK Profiles (av. 21. Oct. – 14. Nov.)

•A control model run without data assimilation was accomplished for the same period of time

Assimilation of the Oct./Nov. 2003 Data



Data Availability 21. Oct. – 14. Nov. 2003

Assimilated

XX

AvailableSCIA-Occ

Assimilated

X

XX

AvailableSCIA-LimbMIPAS-IMK

BrO

AssimilatedAvailable

XXN2O5XNO

XXClONO2XClO

XXCFC-12XXCFC-11XXCH4XXH2O

XHNO4XXHNO3XXN2OXXNO2XXO3



MIPAS FZK-IMKClONO2 retrieval example

Distribution of ClONO2 at 20 km altitude in the Southern hemisphere for 24 July 2002 and 3 days in September 2002 (case study 1). Note the pronounced collar structure at the vortex edge.



SCIAMACHYSolar Occultation

Data Analysisfor SACADA

J. Meyer, A. Bracher, L. Amekudzi, S. Noel, A. Rozanov, B. Hoffmann, H. Bovensmann, J. P. Burrows

Institute for Environmental Physics, University of Bremen, Germany

O3 and NO2



Results: Cost-functions for Oct. 21 – Oct. 26

χ2- optimum



Results: Cost-function for Oct. 27 – Nov. 14

χ2- optimum



Scatter plots for Nov. 13, 2003

Control Run(after 23 days of free integrationwith SOKRATES(2D) initial values)

23 days consecutive 4D-var:

Background

Analysis

HNO3 ClONO2 O3

observations

observations

mod

el

mod

el

mod

el



Control Run

(no assimilation)MIPAS Observations Analysis

Results for ClONO2 at 7.6 hPa (~33 km), Nov. 13, 2003 12:00 UTC



Control Run

(no assimilation)AnalysisMIPAS Observations

Results for HNO3 at 28 hPa (~24 km), Nov. 4, 2003 12:00 UTC



water vapour assimilation

Integration start:1. July,

no assimilation

20. July 2003~160 hPa

after continuous assimilation

MIPAS water vapour retrievals



Assimilated

MIPAS profiles

for Nov, 13. 2003 at 57°E/2°N

Analysis: Blue solid line

Background: Black dotted line



Ozone profiles averaged over the latitude belts indicated and the time span 8.9.-15.10.2002



RMS of analysis model state against observations

HNO3

ClONO2

O3

CH4

N2O

H2O

N2O5

NO2

controlanalysis



Bias of analysis model state against observations

HNO3

ClONO2

O3

CH4

N2O

H2O

N2O5

NO2



HNO3

ClONO2

O3

O-F differences (left column) and

O-A differences (right column)

Dotted line represents a Gaussian with same variance as the data



Comparison between OI and 4D-var based analyses: ROSE - SACADA

Cou

rtesy

Bai

eret

al.,

200

6, E

GU

pos

ter

ROSE: 2.5°x3.8° SACADA: ni32 = 2.25x2.25



Diffusion can be generalised to account for inhomogeneous and anisotropic correlations:

η field of observation incrementsHTR(y-Hx)

use PV field Π for anisotropiccorrelation modelling



Comparison between homogeneous/isotropic and inhomogeneous/anisotropic covariance modelling

MIPASretrievals

PV field

homogeneousandisotropic

inhomogeneousandunisotropic



Status on stratospheric chemistry data assimilation

• Stratospheric data assimilation systems with reactive chemistry are prepared to ingest all available routine data,

• The retrieval level is still 2 (geolocated data) or averaging kernels

• So far only a small fraction of all available data has been used

• Routine operations and archives with analyses still need further developments

lecture 3 stratospheric chemistry data assimilation

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