IASI Level 2 Processing

Transcription

1 IASI Level Processing Peter Schlüssel EUMESA Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide:

2 Outline IASI instrument properties IASI Level processor overview IASI Level physical retrieval Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide:

3 Metop: Satellite and Instruments AVH-3 GAS GOME- HIS-4 Metop only IASI Level Processing Stability and Uniqueness IASI AMSU-A ASA MHS AMSU-A Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 3

4 IASI Instrument Overview Pixel diameter km at nadir Sampling interval 5 km at nadir Field of view ±48.33 Spectral range 645 to 760 cm Spectral resolution 0.35 to 0.5 cm adiometric resolution 0.5 to 0.5 K Design lifetime 5 years Power consumption 00 W Dimensions. m x m x. m Mass kg Data rate.5 Mbit/s Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 4

5 Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 5

6 Instruments Fields of View IASI AMSU-A MHS HIS/4 AVH/3 Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 6

7 IASI Spectral Bands Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 7

8 Instrument oise at Different emperatures Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 8

9 Operational IASI Level Processor he IASI Level product processing facility (PPF) is being built by industry in the frame of the EPS ore Ground Segment development according to specifications given by EUMESA he specifications have been written according to current scientific knowledge as expressed in literature results from scientific studies heritage of AIS and input from review by ISSWG and IF Document: EPS Ground Segment: IASI Level Product Generation Specification (current release: Issue 5 ev. 4) Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 9

10 Properties of the Operational IASI L Processor ombined use of IASI AVH AMSU-A MHS and AOVS Level Optional inclusion of WP forecast is possible Flexibility due to steering of the processing options by configuration settings (86 configurable auxiliary data sets) allows for optimisation of PPF during commissioning Online quality control supports the choice of best processing options in case of partly unavailable or corrupt data Besides error covariances a number of flags are generated steering through the processing options and giving quality indicators (4 flags are specified) which are part of the product Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 0

11 Properties of the Operational IASI L Processor (cont.) All IASI channels can be used in the retrieval to maximise the retrieved information ν k = ( k + ) 0.5 cm k = 846 PPF supports nominal and degraded instrument modes (e.g. failure of single detectors) Bias control by radiance tuning via configuration Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide:

12 IASI Level Product Generation High Level Break-Down Structure IASI Level AVH loud Mask and S/ onfigurable Databases AOVS Level AMSU-A Level MHS Level WP Forecast Pre-Processing loud Processing Geophysical Parameters etrieval Monitoring Information Level Product Quality Information Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide:

13 Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 3 Geophysical Parameters etrieval State Vector to be etrieved ) ( φ α α α cld cld cld S S O H O O p p p O O W W O W x =

14 Geophysical Parameters etrieval First etrieval EOF regression for temperature and water-vapour and ozone profiles surface temperature and surface emissivity Artificial neural network (multi-layer perceptron) for trace gases (optionally also for temperature water-vapour and ozone depends on configuration setting) he results from the first retrieval may constitute the final product or may serve as input to the final iterative retrieval the choice depends on configuration setting and on quality of the first retrieval results Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 4

15 EOF egression etrieval n = n m + L i= n i m ( s i m s i m ) s K i m = ( bk m bk m) k = k i m n = retrieved deviation from mean temperature profile (incl. surface temperature) nim = covariance operators at level n eigenvector i and surface type m s im = principal component scores bkm = measured IASI brightness temperature spectrum E kim = precalculated eigenvectors Analogue expressions for water vapour ozone emissivity Pre-calculated operators eigenvectors and mean quantities are configurable E Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 5

16 Artificial eural etwork for race Gas olumns S S = w f w f w k kj ij r r + M " ( i) + w ( i + + j k ji r + i= r + ) k = j= i= f ( z) = tanh( z) etrieval of columnar trace gas amounts from a sub-set of r 00 IASI radiances (i) and a coarse temperature profile (i r ) defined on M 0 levels using a multi-layer perceptron with S and S neurons in the first and second hidden layers respectively Weights and number of neurons are configurable he coarse temperature profile will be taken from the first retrieval Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 6

17 Physical etrieval Simultaneous iterative retrieval seeking maximum probability solution by Marquardt-Levenberg method (combination of steepest descent and ewtonian iteration) using a subset of IASI channels combined to super-channels Initialisation with results from first retrieval Other choices of initialisation may be selected depending on configuration setting (e.g. WP forecast climatology) Background state vector from climatology AOVS adjacent retrieval or WP forecast depending on configuration State vector to be iterated depends on cloud conditions and configuration setting (clear cloudy variational cloud clearing) Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 7

18 Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 8 Geophysical Parameters etrieval State Vector in lear and loudy ases lear case single IFOV loudy retrieval single IFOV ) ( 4 S S O H O O O O W W O W x = Variational cloud clearing IFOV quadruples ) ( cld cld cld S S O H O O p p p O O W W O W α α α x = ) ( 3 4 η η η S S O H O O O O W W O W x =

19 Preparation of Variational loud learing Inclusion of cloud-clearing parameters η ι0 in variational retrieval allowance for up to three cloud formations etrieval for combination of IFOV-quadruples in an EFOV clr clr clr = η 0 η 0 η3 First guess of clear radiances from calculations based on AOVS profiles Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 9

20 Maximum Probability Solution Formulation of ost Function Minimisation of cost function J = m ( y( x) y ) E ( y( x) y ) + ( x x ) ( x m b x b ) x = atmospheric state vector as calculated iteratively x b = atmospheric state vector from background field = the error covariance matrix associated with the background y m = measurement vector E = combined measurement and forward model error covariance matrix y(x) = forward model operator for given state vector x K x = partial derivatives of y with respect to the elements of x (Jacobians) Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 0

21 Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: Maximum Probability Solution Minimisation of ost Function ) ( J I J x + = ξ δ ) ( ) ) ( ( b i m i x x x y x y E K J + = x x K E K J + = x x n K E K S + = ) ( Increment after each iteration step with Error covariance of the iterated solution

22 Maximum Probability Solution onstraints on Iteration and onvergence Super-saturation only within the error bounds of the temperature profile Super-adiabatic layering only within the error bounds of the temperature profile Iteration step is only accepted if cost function decreases ermination of iteration according to following criteria or after given number of iterations x n n ( J n J n) J n < tχ or J n < tχ or < tχ 3 xn he termination threshold and the maximum number of iterations are configurable x Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide:

23 Parameters Steering the Physical etrieval ontrol Parameter [ Initialised by mean diagonal of covariance matrix multiplied by a constant factor Updated according to convergence criteria: If cost function increases step is not taken ξ is changed towards steeper descent If cost function decreases step is taken If change is greater than a threshold ξ is changed towards more ewtonian If change is smaller than a threshold ξ is kept constant Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 3

24 Parameters Steering the Physical etrieval Background Vector and ovariance limatology is taken as background constraint as WP forecast is not desired in retrieval Problem: climatology imposes strong constraint with respect to vertical correlation relaxation on the vertical correlation will be necessary Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 4

25 etrieval Experiments hoice of 95 IASI channels with well distributed weighting functions representing the atmospheric state to be retrieved Level product shall be independent of WP forecast egularisation by climatology or related smoothness-function Physical retrieval is started with a first guess from an EOF regression retrieval using all IASI channels Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 5

26 Selection of 95 IASI hannels B (K) Wavenumber (cm - ) Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 6

27 Same Atmospheric Situation Measured in 4 IASI IFOVs with Different E' from Same distribution p (hpa).05 First etrieval p (hpa).05 Iterative etrieval (K) (K) Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 7

28 Problem A small perturbation in the first guess profile is strongly amplified by the physical retrieval leading to an unrealistic result How to cure? ighten the a priori constraint (e.g. apply further constraints such as no super-saturation and no super-adiabatic layering) hose better representation of state vector (e.g. deep layer means) Improve initial choice of ξ: right balance between steepest descent and ewtonian iteration emoval of components in/near null space elax measurement noise in early iteration steps (cost is too high) What is useful for high-resolution sounding? Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 8

29 Uniqueness omponents of the state which are in the row space are likely to be measured well Little a priori constraint necessary example: surface temperature measured with window channels omponents of the solution which are in the null space or near-null space cannot be retrieved from the measurements emoval of such components as far as possible Introduction of strong a priori constraint where unavoidable example: high-resolution profiles If components which lie in the null space are included the retrieval and are not strongly constrained by a priori will produce non-unique results Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 9

30 Problem Application of strong constraints like climatology prevent the physical retrieval from observing fine-scale structures in the profiles If fine structure is present in the first guess it will be taken up by the physical retrieval and can be modified according to the measurements Lacking fine structure in the first guess is not recovered by the physical retrieval if the background constraint prescribes close correlation between adjacent or distant layers Way forward elax background constraint at the cost of increased retrieval errors Profile will show much fine-structure User will need full covariance matrix to assimilate such profiles which do not look realistic Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 30

31 Effect of a priori onstraint rue Profile Full covariance Diagonal ovariance p (hpa) p (hpa) (K) Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 3

32 Effect of a priori onstraint p (hpa).05 rue Profile Full covariance Diagonal ovariance p (hpa) (K) Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 3

33 General Observations he physical retrieval improves over the statistical retrieval (used as first guess) Fine structure is rarely added by the physical retrieval if climatological background is used Strong background/a priori constraint tends to suppress fine structure which was in the first guess Soundings from High Spectral esolution Observations Madison May 003 EUM.EPS.SYS Issue Slide: 33