lecture 3 stratospheric chemistry data assimilation
TRANSCRIPT
Day 5 Lecture 3 Stratospheric Chemistry Data Assimilation Hendrik Elbern 1
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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
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Objectives of this lecture
Understand • different atmospheric chemistry applications
• required special treatments for the stratosphere
• the differences to meteorology
• some advanced examples
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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
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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
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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
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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
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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)
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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.
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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.
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TransportTransport--diffusiondiffusion--reactionreaction equationequation and and itsitsadjointadjoint
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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??
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An example:
The operartional middle atmosphere data assimilation system for
Synoptic Analyses of Chemical constituents by Advanced Data Assimilation
SACADA
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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
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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)
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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
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General Chemistry Circulation Model Structureonly 1x per
assimilation
n~20x(each iteration)
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SACADA storage and recalculation strategysi
ngle
forw
ard
time
step
sing
le a
djoi
nt/b
ackw
ard
time
step
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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
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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
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Heterogeneous chemistry formulation(direct and adjoint)
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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)
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B1/2 and BT/2 describing a quasi Gaussian correlationcan be modelled using a diffusion operator:
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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.
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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
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•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
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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
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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.
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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
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Results: Cost-functions for Oct. 21 – Oct. 26
χ2- optimum
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Results: Cost-function for Oct. 27 – Nov. 14
χ2- optimum
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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
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Control Run
(no assimilation)MIPAS Observations Analysis
Results for ClONO2 at 7.6 hPa (~33 km), Nov. 13, 2003 12:00 UTC
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Control Run
(no assimilation)AnalysisMIPAS Observations
Results for HNO3 at 28 hPa (~24 km), Nov. 4, 2003 12:00 UTC
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water vapour assimilation
Integration start:1. July,
no assimilation
20. July 2003~160 hPa
after continuous assimilation
MIPAS water vapour retrievals
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Assimilated
MIPAS profiles
for Nov, 13. 2003 at 57°E/2°N
Analysis: Blue solid line
Background: Black dotted line
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Ozone profiles averaged over the latitude belts indicated and the time span 8.9.-15.10.2002
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RMS of analysis model state against observations
HNO3
ClONO2
O3
CH4
N2O
H2O
N2O5
NO2
controlanalysis
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Bias of analysis model state against observations
HNO3
ClONO2
O3
CH4
N2O
H2O
N2O5
NO2
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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
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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
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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
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Comparison between homogeneous/isotropic and inhomogeneous/anisotropic covariance modelling
MIPASretrievals
PV field
homogeneousandisotropic
inhomogeneousandunisotropic
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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