Atmospheric sounding using IASI2nd Year Report11103 words

Lucy VentressAOPP, University of Oxford

August, 2011

Contents

1 Introduction 2

2 The Infrared Atmospheric Sounding Interferometer 4

3 Theory 73.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.1 Optimal Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.1.2 The Averaging Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.1.3 The Forward Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.1.4 Figures of Merit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.1.5 Column amount . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4 Channel Selection for use in Numerical Weather Prediction 114.1 Channel selection methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.1.1 Main selection algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.1.2 The Collard Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.1.3 Including Systematic Error . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.2 Example - Replication and comparison . . . . . . . . . . . . . . . . . . . . . . . . . 134.2.1 Replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.2.2 Systematic Error Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 134.2.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.3 Alternative Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.3.1 CH4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.3.2 O3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5 Channel Selection for Trace Gases 235.1 Impact of Stratospheric knowledge on Tropospheric Retrievals . . . . . . . . . . . . 23

5.1.1 Limb-viewing Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235.1.2 Atmospheric Sounding of Methane, CH4 . . . . . . . . . . . . . . . . . . . . 245.1.3 Tropospheric Sounding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6 Future Work and Thesis Plan 30

1

Chapter 1

Introduction

Atmospheric observations are vital in the process of weather forecasting. (It is only with a largerange of observational data monitoring the Earth’s atmosphere, land and ocean that forecastscan be produced.) Presently observations come from several sources: radiosondes, surface data,aircraft observations and satellites. Satellites have become the prevalent source of data due tothe quantity they produce, providing measurements of atmospheric properties such as pressure,temperature, humidity and wind with almost global coverage

Although satellites provide almost global coverage, there are still data gaps within NumericalWeather Prediction windows (typically 6hrs for global NWP). To overcome this problem theprocess of data assimilation is used. This involves combining the observations available with aforecast of what the conditions are expected to be to form a best estimate of the current stateof the atmosphere. This best estimate, or analysis, is the starting point for numerical weatherforecasts, which simulate the Earth’s atmospheric processes that can affect the weather and it isthis analysis that is propagated forward in time to create the forecast.

Infrared sounding instruments, such as the Infrared Atmospheric Sounding Interferometer(IASI), which will be discussed in chapter 2, can provide data with high spectral resolution. Alarge number of weather centres now use the spectral data from IASI and its use has been shownto have a significant positive impact on global Numerical Weather Prediction (NWP). This impactbecame apparent shortly after IASI’s launch despite its data initially having a fairly conservativeweighting during the assimilation process [1]. Its absolute radiometric accuracy has been shownthrough comparisons with data from AIRS (Atmospheric InfraRed Sounder) and the 11 micronAATSR (Advanced Along-Track Scanning Radiometer) channel, in both cases agreement beingbetter than 0.1K, [2] [3].

IASI measures the radiance emitted from the Earth and atmosphere. In a cloud free regionthe radiance can be measured throughout the atmosphere. This allows vertical information to bederived for certain species, assuming surface parameters such as temperature and emissivity andthe atmospheric temperature are known or simultaneously retrieved. The large number of channelsavailable from the IASI instrument leads to a very large quantity of data. However, this can leadto problems within retrievals and data assimilation. Choosing an optimal subset of the channels isan established method to reduce the amount of data, whilst maintaining the information containedwithin it [4]. In selection, the amount of information retrieved needs to be optimised whilst alsoeliminating spectral regions with large uncertainties. The method currently used at the UK MetOffice to select their channels is discussed in chapter 4 along with an alternative method withpotential to improve upon the influence of systematic errors.

Although the number of IASI’s spectral channels used in NWP has increased since its launch,the maximum is still at approximately 200 channels. The majority of these lie in the long waveCO2 band and provide atmospheric temperature information, whilst the IASI water vapour bandis currently under used. Only a fews tens of the water vapour channels are assimilated. Reasonssuggested for this limited use include difficulties with the observation error correlations and biasesin the assimilating NWP models [1].

2

Principal Component Analysis (PCA) is a method of projecting spectra onto a principal compo-nent basis and is being explored for use in data compression. Principal Component Compression(PCC), as an alternative to channels selection, has shown powerful properties of noise filteringwhilst minimizing the loss of information on trace gases and species, and a strong compressionrate [5]. The compressed PC spectra are disseminated in parallel with the full IASI radiancespectra. However, attention must be paid to ensure that all relevant information is kept and alsothat certain events, such as biomass burning or pollutant plumes, are not missed. This dependsupon similar features being included in the ‘training set’ of spectra used to create the PCs andthe number of PCs chosen for compression. It remains to be investigated whether or not trace gasretrievals can benefit from the increased signal to noise ratio in the reconstructed radiances and ifthe PC scores will contain enough information for applications such as chemistry monitoring or ifthe full IASI spectra will be needed. Although investigated briefly as an alternative method, PCAis not the focus of this report.

The primary instrument in my project is IASI, a nadir viewing instrument. Chapter 5 con-siders the impact of combining measurements from nadir instruments, such as IASI, alongsideinstruments with different viewing geometries, and whether this can improve retrieval accuracy.For example, limb viewing instruments such as MIPAS (the Michelson Interferometer for PassiveAtmospheric Sounding) provide good vertical resolution but only in the upper troposphere andstratosphere. Nadir viewing infrared instruments, including IASI, have given global concentra-tions but these are limited to the mid to upper troposphere and lower stratosphere [6]. However,large variability can be seen in the lower troposphere and boundary layer when close to emissionsources. This study looks at how the potential of using different viewing instruments togethermay improve our knowledge of this region and our retrievals of tropospheric concentrations.

Contained in this report is the work carried out during the second year of my study, primarilyinvestigating methods of channel selection and how these methods can be improved upon.

3

Chapter 2

The Infrared AtmosphericSounding Interferometer

The Infrared Atmospheric Sounding Interferometer (IASI) is one of eleven instruments on boardthe METOP-A satellite that was launched on 19th October 2006. It was developed by CNES(Centre National d’Etudes Spatiales) for use as part of the EUMETSAT (European Organisationfor the Exploitation of Meteorological Satellites) European Polar system. Two more instrumentsin the same series of satellites are planned to be mounted on METOP-B and METOP-C, due tobe launched in 2012 and 2016 respectively, allowing the IASI instrument to provide observationsover a long timescale.

The IASI instrument is an infrared Fourier transform spectrometer associated with an imagingsystem, which uses nadir viewing geometry passively to measure the radiance from Earth. Theradiation emitted from both Earth and the atmosphere is affected by the emission, absorptionand scattering of the atmospheric molecules along its optical path. It is the radiation resultingfrom these interactions that IASI measures as the atmospheric spectrum, containing atmosphericemission/absorption features [7]. The instrument is based on a Michelson interferometer: thisdecomposes the emitted spectrum, after which an inverse Fourier transform and radiometric cali-bration are applied. The calibrated data are subsequently transmitted to the ground segment [8].The essential components of the instrument, shown in figures 2.1 and 2.2 are discussed in detailelsewhere, ([8] [9] [10] [11]), but include:

• A scanning mirror.

• An afocal telescope to transfer the image onto the scan mirror.

• The Michelson interferometer, including a beam splitter and corner cube mirrors that aredesigned to create the optical path difference necessary for the specified spectral resolution.

• The cold box, which the recombined beams are directed into, containing refractive opticsthat divides the spectral range into three bands.

• The Digital Processing Subsystem that carries out the inverse Fourier transform and cali-bration on the interferograms before being transmitted to the ground.

The main characteristics of the IASI instrument are summarised in Table 2.1. IASI covers thespectral range 645–2760 cm−1 with a spectral resolution of 0.25 cm−1 (0.5 cm−1 apodized) givinga total of 8461 channels in each IASI spectrum. This results in a large quantity of data thatcan cause problems for both retrievals and data assimilation. Choosing an optimal subset of thechannels is an established method to reduce the amount of data and is discussed in chapter 4. Thespectral range is divided into three bands as shown in figure 2.3. ‘Band 1’, from 645–1210 cm−1,is primarily for temperature and ozone sounding, ‘Band 2’, from 1210–2000 cm−1, is primarily

4

Figure 2.1: An internal view of IASI components - View 1. (Picture by CNES, [11])

Figure 2.2: An internal view of IASI components - View 2. (Picture by CNES, [11])

for water vapour sounding and retrieving N2O and CH4 column amounts and ‘Band 3’, from2000–2760 cm−1, is for temperature sounding and the retrieval of N2O and CO column amounts[13].

IASI achieves global coverage and provides measurements twice a day at each location, at alocal time of 09:30, from a sun synchronous orbit. The instrument scans perpendicularly to themotion of the satellite between angles −47.85◦ and +47.85◦, with respect to the nadir. Withineach scan there are 30 steps at which measurements are taken, as well as views to the calibrationtargets: an internal hot black body and cold deep space. Each view consists of a 2 × 2 matrixcontaining independent circular pixels with diameters of 12 km, as shown in figures 2.4a and2.4b, to increase the probability of obtaining cloud free views. The IASI scan pattern providesmeasurement locations compatible with other instruments on board the METOP satellite [9].

5

Table 2.1: IASI Instrumental Characteristics (taken from Clerbaux et al. [12])

Characteristics

Spectral Range 645–2760 cm−1 (15.5–3.62µm)Spectral Resolution 0.5 cm−1 (apodized)Instrumental Noise 0.2 to 0.35K (NEDT at 280K)Pixel Size Diameter of 12 km, 4 pixels matrix,

across-track scanningData Rate 1.5 megabits per secondLifetime 5 years

Altitude ∼ 817 kmOrbit polar sun-synchronousInclination 98.7 ◦ to the equatorLocal time ∼ 09:30Orbital Period 101 min

Figure 2.3: A typical IASI spectrum and its spectral bands

Figure 2.4: The IASI field of view. (Pictures by CNES, [11])

6

Chapter 3

Theory

3.1 Background

In this chapter the theoretical basis of the work contained in this report is discussed.

3.1.1 Optimal Estimation

This study was based on the use of optimal estimation as described by Rodgers, 2000 [14]. Herethe notation and definitions are briefly explained.

The atmospheric profile, in terms of temperature or the concentration of a particular atmo-spheric molecule, at a given location, is represented by the state vector x. The satellite measure-ments, i.e. brightness temperature spectra, are represented by the vector y. These two vectorscan be related by the following equation:

y = F(x) + ε, (3.1)

where ε is a combination of the measurement and forward model errors whose resulting errorcovariance matrix is given by Sy, and F(x) is the forward model function that contains ourunderstanding of the physics of the situation. Assuming the radiative-transfer equation to beweakly non-linear, this can be linearised around a reference state, x0, becoming

y − F(x0) =∂F(x)

∂x(x − x0) + ε = K(x− x0) + ε (3.2)

where K is known as the weighting function matrix, or Jacobian matrix and contains the partialderivatives of each forward model element with respect to each state vector element, i.e.

Kij =∂yi∂xj

. (3.3)

The inverse problem is concerned with retrieving an estimate of x from gathered measure-ments, y. Remote sensing is usually an ill-posed problem and cannot be entirely defined by themeasurement, hence, optimal estimation includes the use of a priori values, [7]. These can be anypreviously known information about the atmospheric conditions that is unrelated to the measure-ments taken, such as climatology. In optimal estimation a solution is found that minimises thesquare of the differences between both the measurements and the forward model and the a priorivalues with the estimate of the state variable. i.e.

χ2 = (y −Kx)T S−1y (y −Kx) + (x− a)T S−1

a (x− a) (3.4)

is minimised, where a is the a priori estimate of the solution and Sa and Sy are the a prioriand measurement/forward model error covariance matrices respectively. If the inverse a priori

7

error covariance matrix, S−1a , is set to zero then only the measurements are used to calculate the

retrieval and no prior knowledge of the system is assumed. This reduces the problem to a weightedleast squares fit retrieval. Once minimised, in the linear limit, equation 3.4 allows the retrievedstate vector to be expressed as

x = (KT S−1y K+ S−1

a )−1(KT S−1y y + S−1

a a), (3.5)

which can also be written in the form,

x = Gy +Ha, (3.6)

= Gy + (In −GK)a (3.7)

in terms of the gain matrices

G = (KT S−1y K+ S−1

a )−1KT S−1y , (3.8)

H = (KT S−1y K+ S−1

a )−1S−1a , (3.9)

where G is how the retrieval depends on the measurements and H is how the retrieval dependsupon the a priori. The assumption that the measurements and a priori are uncorrelated with eachother is made allowing the corresponding state vector error covariance matrix, Sx, to be writtenas

Sx = GSyGT +HSaH

T, (3.10)

which reduces to,Sx = (KT S−1

y K+ S−1a )−1. (3.11)

3.1.2 The Averaging Kernel

The averaging kernel matrix, A, relates the sensitivity of the retrieval to the true state. Itselements are defined to be

Aij =dxi

dxtj

, (3.12)

where xt is the true value of the state. This arises from the substitution of y = Kxt into equation3.6,

x = GKxt +Ha. (3.13)

It can be shown that GK+H = I (where I is the identity matrix), which allows x to be expressedas

x = GKxt + (I−GK)a, (3.14)

= Axt + (I−A)a, (3.15)

and A to be defined asA = GK. (3.16)

3.1.3 The Forward Model

Radiative transfer models attempt to calculate accurate top of atmosphere radiances, given at-mospheric profiles of the gases present. Their aim is to solve the equation of radiative transfer.The spectrum is calculated as a set of radiances at each spectral point on a chosen grid. Thisis convolved with the channel’s corresponding spectral response function to obtain the simulatedradiance expected from a particular instrument. There are two main types of radiative transfermodels: line by line radiative transfer models and fast radiative transfer models. Although lineby line models produce much more accurate results they are very computer processing heavy andcannot be run in near real time. For this reason, fast radiative transfer models were developed foruse in numerical weather prediction.

8

The Reference Forward Model, or RFM, is a line by line model developed at the University ofOxford [15]. It is based on the line by line model GENLN2 [16] and was developed originally foruse with the Michelson Interferometer for Passive Atmospheric Sounding, MIPAS, but has sincebeen developed into a general purpose code for many spectroscopic calculations. Line by linemodels perform accurate calculations of atmospheric radiances and transmittance at high spectralresolution.

The RFM is the forward model used in this report to simulate the atmospheric spectra andcreate the Jacobian matrix, K, which in turn is used to calculate the error covariance matrix of thestate vector as shown in section 3.1.1. In the case of atmospheric gases, the Jacobian calculatedby the RFM is the rate of change of the brightness temperature spectrum to a 1% perturbationof the state vector, i.e.

K =δTb

δx[%]= Tb(x[ppm]× 1.01)− Tb(x[ppm]) (3.17)

where the state vector can either be the total column amount of a particular gas or the concen-tration of a gas at different levels in the atmosphere, and hence, when running the forward modelthe perturbation can be applied to the column as a whole or to each individual layer (of specifiedthickness) respectively. When calculating the Jacobian for atmospheric temperature, the rate ofchange due to a 1K perturbation to the state vector is calculated.

3.1.4 Figures of Merit

A figure of merit is a quantity that characterises performance: for example, how much informationcan be gained by the additional use of a measurement channel. Information in the retrieval errorcovariance matrix is compressed into a single scalar quantity allowing the intercomparison ofdifferent covariance matrices (e.g. from using different measurement channels).

Two figures of merit generally used are the Shannon information content and the degrees offreedom for signal (DFS). The former is defined (e.g. Rodgers, [14]) to be the factor by whichknowledge of a quantity is improved by making the measurement and aims to improve the erroranalysis at any point in the profile regardless of previous knowledge at that level:

H = −1

2ln |In −A| = −1

2ln|Sx S

−1a |, (3.18)

where In is the identity matrix and A is the averaging kernel matrix.Alternatively, the the degrees of freedom for signal, ds can be used, which is essentially the

number of independent pieces of information you have in an estimate of a measurement. Itattempts to spread the information over the profile, favouring levels where there is a lack ofknowledge.

ds = tr([KT S−1y K+ S−1

a ]−1KT S−1y K), (3.19)

= tr(GK), (3.20)

= tr(A), (3.21)

where G is the gain matrix (the generalised inverse of K). The DFS are chosen for this study sothat the profle as a whole is improved and not just certain levels.

3.1.5 Column amount

Column amount is a measure used to describe the quantity of a specified gas in the atmosphere.It is the amount of the gas in a vertical column of unit cross-sectional area that extends from theEarth’s surface to the top of the atmosphere. It can be defined as:

∞∫

0

x(z) ρ dz, (3.22)

9

where x(z) is the state vector as a function of height, z and ρ is the air density. When discussingthe column amount of a gas, most generally ozone, a common unit used is the Dobson unit, DU.This is the height that the column of gas would have if it were compressed to standard temperatureand pressure and gives the conversion 1DU = 2.69 × 1016molecules cm−2. The Dobson unit isused throughout this report when referring to the column amount.

The RFM produces the rate of change of the brightness temperature to a 1% perturbation ofthe state vector as described in section 3.1.3, where the state vector has the units of ppm (partsper million). The conversion from ppm to Dobson’s units is as follows:

c[DU] =1

2.69× 1016

∞∫

0

x[ppm]× ρair dz (3.23)

=1

2.69× 1016

p0∫

0

x[ppm]

gdp, (3.24)

where the latter results from hydrostatic equilibrium and c is the column amount.The column amount can be calculated from the retrieval of the atmospheric profile in layers of

a set thickness using the above equation. When carrying out the integral numerically, the extendedtrapezoidal rule is implemented, i.e.

pn∫

p1

x(p) dp ≈ ∆p[1

2x1 + x2 + ....+ xn−1 +

1

2xn], (3.25)

where n is the number of levels in the atmosphere and ∆p is the height (in pressure units) of eachlayer. However, when integrating over pressure co-ordinates, the heights of each of the layers arenot equal and the integral becomes:

pn∫

p1

x(p) dp ≈n∑

i=1

(xi + xi+1

2

)(pi+1 − pi). (3.26)

Following from the above equations, an alternative way of calculating the column amount, c,and its associated covariance error, Sc, is to express them as

c = a1x1 + a2x2 + . . .+ anxn (3.27)

= aT · x (3.28)

Sc = σ2c (3.29)

= aT · Sx · a (3.30)

where the ai values are constants that represent the transformation from the concentrations indifferent layers to column amount.

There are two possible forms of the state vector; a column amount value or a vertical profile. Ifworking directly with the whole atmosphere (a column amount state vector), the calculated errorcovariance, Sx, is a scalar and equals the column amount error covariance, i.e. Sx = Sc and hencethe total column amount error is purely the square root of this value, see equation 3.29. However,when working with an atmospheric profile the error covariance must be first transformed into atotal column amount covariance error using equation 3.30.

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Chapter 4

Channel Selection for use inNumerical Weather Prediction

4.1 Channel selection methods

The large number of channels available from the IASI instrument leads to a very large amount ofdata. Choosing an optimal subset of the channels is an established method to reduce the amountof data, [4]. In this section methods for the selection of this subset are discussed and compared.

4.1.1 Main selection algorithm

The channel selection method used by Collard [4] was based on that given by Rodgers [14] andwas shown by Rabier et al. [17] to be the best selection method for the IASI instrument despitethe expense in terms of computer processing time.

The selection method is based on linear optimal estimation theory for use in Numerical WeatherPrediction (NWP), which was discussed is chapter 3.1.5. It was shown that the state error covari-ance matrix, Sx can be written as,

Sx = (KT S−1y K+ S−1

a )−1. (4.1)

The ‘iterative method’ calculates the impact of each channel upon a figure of merit, assessinghow much information can be gained by the additional use of a measurement channel. The figureof merit used in this study is the degrees of freedom for signal (DFS) given by,

ds = tr(In − SxS−1a ) (4.2)

which attempts to spread the information over the profile, favouring levels where there is a lackof knowledge.

The method proceeds as follows:

1. Begin with an assumption of Sa, e.g. a forecast model covariance.

2. Sx and subsequently the DFS can be calculated individually for each channel and that withthe largest DFS is selected.

3. Sa is updated to contain the information gained from the previously selected channel(s), i.eSa = Sx.

4. The process is repeated from step 2 selecting the next best channel from those remainingthat most improves the figure of merit until a set threshold has been reached, for example,a particular number of channels or a specific amount of information.

11

A variant on this method suggested by Rabier et al., 2002 [17] that decreases the processingtime is in principle the same but does not update the error covariance matrix for each selectionthroughout the analysis. Therefore, any selection made does not take into account the informationgained from previously selected channels. Despite the increase in speed of selection given by thisvariant, during this study the method initially described was the method chosen to use in orderto achieve the greatest accuracy.

4.1.2 The Collard Method

In the method used by Collard [4], before using the main slection algorithm described in section4.1.1, channels are removed based on certain criteria in an effort to avoid spectral regions withlarge uncertainties. There are several pre-screening approaches considered, although not all areused. Channels that are stated to be avoided are those highly sensitive to radiative-transfer modelerrors or species whose variability is not considered in the background or analysis, or have knownradiative transfer weaknesses.

In reality, this means that a channel is ‘blacklisted’ if the effect on the brightness temperaturedue to the climatological variability of a trace gas is greater than 1K. In the example given byCollard this includes CH4, CO and N2O. If the impact was less than 1K then the effect of the tracegas is added to the forward-model error covariance matrix. Channels affected by solar radiation (λ< 5µm) are also ‘blacklisted’ so as the channels selected are not limited to use solely at nightime.

4.1.3 Including Systematic Error

In the new method described in this study, an effort is made to eliminate the need for ‘ad hoc’ pre-screening and instead attempts to include the systematic errors due to the trace gases within theselection process. Taking the total state error covariance matrix to be a combination of the randomerror component, given by Eq. 3.11, and a systematic error component, containing contributionsfrom influences such as trace gases and non thermal equilibrium (NTE) effects, we obtain

STOTx = SRND

x + SSY Sx (4.3)

where SRNDx = (KT S−1

y K+ S−1a )−1 and the systematic component is given by

SSY Sx =

∑

i

(δxSY Si)(δxSY Si)T. (4.4)

This is similar to the error method used by Eremenko et al. in calculating the total errors for anOzone retrieval [18]. Mathematically, the change in the state vector due to a systematic error is

δxSY Si = GδySY Si + (In −GK)δxSY Sia (4.5)

from Eq. 3.6. In the first instance the a priori is assumed to have no systematic errors: δxSY Sia is

assumed to be zero. However, once in the ‘iterative’ part of the selection algorithm it is updatedeach time with the change in state vector due to the errors, i.e. δxSY Si

a = δxSY Si , in a similarfashion to the update of the a priori random error covariance matrix to contain the new informationgained from the selected channel. In this method the a priori error covariance matrix becomesthe random component of the state error covariance matrix, i.e. SRND

a = SRNDx each iteration.

The measurement change due to the effect of systematic errors, δySY Si , is the brightnesstemperature difference between spectra produced from a profile perturbed by the atmosphericvariability of the species and an unperturbed profile calculated using the RFM.

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4.2 Example - Replication and comparison

In order to compare the current selection with the proposed improved method, the practicalselection of 300 channels carried out by Collard has been recreated and subsequently carried outwith the new method. It should be noted that there are some small differences between theoriginal selection and the replication, for instance, this study uses the Reference Forward Model(RFM) [15] for radiative transfer calculations whereas the original used RTIASI-4, ECMWF’s fastRadiative Transfer model for IASI ([19], [13]), also the background error covariance matrix willdiffer even though both are based upon operational 1D-Var matrices. It is thought that these willnot have a large impact upon the result but for consistency, when comparing the two methods,the channels selected by the recreated method will be used rather than those actually picked byCollard.

4.2.1 Replication

The following example shows the selection of 300 channels for distribution to NWP centres innear real time. This means that the only species of interest in the channels selected are watervapour, ozone, carbon dioxide and temperature. The same channel flags used by Collard (per-sonal communication) are used to ‘blacklist’ the channels whose climatological variability cause abrightness temperature change of > 1K. For those whose impact was greater than 1K, CH4, COand N2O, the channels affected were ‘blacklisted’, along with channels influenced by the surface,by non-LTE effects, by solar irradiance and in certain selection runs, by water vapour and ozone.However, the effects from the majority of species examined were less than 1K. Their effect wasinstead added to the combined measurement and forward model error covariance matrix. Herethe impacts of the trace gases to be included in this manner, CCl4, CFC-11, CFC-12, CFC-14,HNO3, NO2, OCS, NO and SO2, are calculated using the six standard FASCODE atmospheres(Mid-latitude daytime, Tropics, Mid-latitude Summer, Mid-latitude winter, Sub-Arctic summer,Sub-Arctic winter).

The forward-model error is assumed to be a constant 0.2K in brightness temperature spaceand the instrument noise is the CNES estimate post launch from 2008 (Hilton, personal commu-nication). These are combined into a measurement and forward model error covariance matrix,which is assumed diagonal. The background-error covariance matrix is the operational Met Office1D-Var IASI background and the forward model used for the calculation of the Jacobians is theRFM.

The channel selction includes the 6 FASCODE atmospheres and two viewing angles, nadir (0 ◦)and 40 ◦. The DFS for each view and atmosphere are calculated separately but it is the total DFS(the sum of them all) that is used as the figure of merit so that the chosen channels can be asgeneralised as possible and not specific to one atmospheric scene.

Finally, the interferograms produced by IASI are apodized to suppresses sidelobes which wouldotherwise be produced. However, this is at the expense of broadening the central peak and there-fore decreasing the resolution [20]. Hence, although adjacent channels are separated by 0.25 cm−1

the signal becomes spectrally correlated between neighbouring channels and the apodized datahas a spectral response function of width 0.5 cm−1. This means that once a channel is selectedthe neighbouring channels are flagged and cannot also be selected.

4.2.2 Systematic Error Calculation

The systematic error contributions from the absorbing gases, CH4, CO, O3 and N2O are calculatedas the difference in brightness temperature between spectra calculated from a profile perturbedby the atmospheric variability of the absorber and an unperturbed profile. The atmosphericvariabilities were calculated in 2001 for use with MIPAS (Michelson Interferometer for PassiveAtmospheric Sounding). The error contribution from Non-LTE effects was calculated from thedifference between spectra including the effect and without.

13

1000 1500 2000 2500Wavenumber, cm-1

220

230

240

250

260

270

280

Brig

htne

ss T

empe

ratu

re, K

H2O

CO2

O3

CO

CH4

N2O

Figure 4.1: Atmospheric spectra for species whose atmospheric variability cause a brightnesstemperature change of > 1K and hence, are included as systematic errors

Figure 4.1 shows the spectra of the gases considered as systematic errors within this report,and where in the IASI spectral range they lie. When considering temperature (essentially the CO2

spectrum) it can be seen that the largest influences will be from water vapour and ozone, whereaswhen considering water vapour, there will be a contribution from all the considered gases.

For the error due to water vapour in the atmosphere, the variability was taken from the IASI Bmatrix used in the Met Office 1D-Var model, available from NWPSAF (the EUMETSAT networkof Satellite Application Facilities for Numerical Weather Prediction)[21]. If the assumption is madethat all the variability of water vapour is correlated in height through the atmosphere then onlyone perturbation, consisting of the roots of the diagonal elements of the background matrix, needbe considered. However, for the most part this is not the case and would cause an over estimationof the error contribution. Instead 27 separate systematic errors are used. TheBmatrix, containingvalues for the lowest 26 of the 1D-Var’s 43 atmospheric levels (up to ∼ 120mb), is decomposedinto 26 independent contributions, in other words its eigenvectors. The perturbations applied areeach eigenvector multiplied by its associated eigenvalue and can be seen in figure 4.2.

The 27th perturbation considered is to assume that the majority of the variation is within thetroposphere and that errors above this point can be assumed to be uncorrelated and approximately20% of the total concentration at each height.

4.2.3 Comparison

Temperature channels

Initially, only a temperature analysis is considered. In the Collard method all potential blacklistedchannels are excluded whereas in the new method the errors from these absorbers, including watervapour and ozone, are included as systematic errors. By initially only retrieving temperature, thisattempts to ensure that the temperature information gained comes from CO2 channels and notfrom H2O channels. Thirty channels are selected in this manner. Subsequently in the Collardmethod, a further 36 channels are selected having restricted the possible channels to the range707.25–759.75 cm−1, with the intention of improving the information in the troposphere as thishas been seen to have the largest impact (reference?).

An attempt to improve this so that the restriction was less strict was made. This involved

14

Figure 4.2: The 26 independent atmospheric variations of water vapour in the atmosphere

weighting the DFS in the appropriate levels so that channels with information in the tropospheremight be preferentially selected, whilst allowing a channel whose Jacobian did not peak in thisregion to still be selected should its information be significantly greater. Having included thisweighting, to various degrees, it appeared that it made little significant difference to the errorprofile achieved by the different channel selection. This can be seen in figure 4.3, which shows therandom error of a temperature profile using the sets of channels selected using different weightings;the tropospheric improvement is of only 0.01K. Therefore, it was deemed unnecessary to includeit in the new method. The difference between the channels selected by Collard and those selectedin my replication can also be seen. The main discrepancy appears to be in the upper atmosphere,however even here the difference is only of order 0.1K and hence the recreated channels appearto capture the same information.

Figure 4.4 shows the error profiles of the first 66 channels selected in both Collard’s method andthe new method including systematic errors. Both the random errors and the systematic errorsare shown. It can be seen that the Collard channels do have much smaller random errors in thetroposphere, which is essentially why they were chosen. However upon including the systematicerrors with these channels, the errors are significantly increased, with those in the stratospherelittle affected. Likewise, the channels selected with the errors included are little changed in thestratosphere but again have a fairly large change in the troposphere when comparing to the randomerror.

It is also interesting to compare the DFS for each profile as this tells how many pieces ofactual information we can get. The total number of Degrees of Freedom available, which hasbeen maximised in the selection, is for 6 atmospheres and 2 viewing angles. Therefore, the totalavailable per profile is a twelfth of this value.

Table 4.1 shows that when comparing the random error component (essentially only the ra-diometric noise) then both selection methods obtained a very similar value. The Collard methodhas a slightly higher DFS, which is expected as this is the quantity that was maximised for thatselection. However, the fact that both give a similar value shows that there are likely to be severalcombinations of channels that will provide the same quantity of temperature information. Thedifference comes from the effect of interfering gases in these channels. Calculating the effect ofthese systematic errors has little effect upon the DFS for the new method as this is the valuemaximised for that selection. This is due to the selection choosing channels with very small sys-

15

Figure 4.3: Temperature error profiles for 66 channels. All show the random error and havedifferent weightings on the troposphere.

Figure 4.4: Temperature error profiles from 66 channels for the Collard Method (blue) and thenew method (red). The errorprofiles due to both the random error (dashed line) and the combinedsystematic and random errors (solid line) are shown.

tematic errors and therefore the DFS for that channel will be very close to the random DFS forthat channel. However, although the Collard method may choose channels with a slightly betterrandom component, once the systematic errors are included the DFS are drastically reduced bymore than a factor of 2. This implies that the channels selected in the new method should beable to provide the same amount of random information as those currently used but with lesssensitivity to errors caused by trace gases.

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Table 4.1: Degrees of Freedom for Signal available from 66 temperature channels

Total DFS Collard Method New MethodRandom Error Only 77.34 76.28With Systematic Errors 29.61 72.44Without Water Vapour Error 61.13 74.00DFS per profileRandom Error Only 6.45 6.36With Systematic Errors 2.47 6.04Without Water Vapour Error 5.09 6.17

A large increase in error in the upper troposphere in the Collard ‘error’ channels can be seenin figure 4.4 when including the systematic errors. This is expected to be due to one or two ofthe channels being particularly sensitive to water vapour, an error included in the selection of theother set of channels. This can also be seen in the DFS available if the water vapour error isincluded and if it is not, 29.61 and 61.13 respectively. This implies that the Collard channels havea sensitivity to water vapour, but as the main retrieval will also be for water vapour this is notnecessarily a bad thing.

Main Channels

Once the temperature channels have been selected, the ‘main run’ is carried out. This consistsof a joint retrieval of both water vapour and temperature. In the Collard case, the water vapourchannels are un-blacklisted and are now available for selection. The joint retrieval is so thatalthough the channels selected will mainly be sensitive to water vapour they will also provideadditional temperature information, on top of that already gained in the first 66 channels. Thenew method also carries out a joint retrieval, removing the systematic error due to water vapour.In line with the 2007 Collard example a further 186 channels are selected in this manner, givinga total of 252 channels.

The water vapour a priori values come from the Met Office 1D-Var B matrix. Althoughprovided on the Met Office pressure grid, the water vapour only extends to the 26th level (∼120mb). For the height levels above this, the a priori errors were calculated from the standardFASCODE atmospheres and assumed to be uncorrelated.

Firstly, the replication of the Collard selection was carried out and figure 4.5 shows whichchannels were selected. It can be seen, in green, that many of the channels are common toboth the replication (purple) and the original collard selection (orange), especially amongst thetemperature region in the top panel, where 46 out of the 66 selected temperature channels are thesame. The discrepancy in channels appears to be mainly in the selection for water vapour in thelower panels, although despite not selecting the identical channels, very similar or neighbouringchannels are chosen. It is expected that these differences arise due to the subtle unavoidabledifferences in the replication, such as the forward model used and the a priori assumptions. Sincethese factors will not vary between the new systematic error method and the replication, fromthis point all comparisons are to the recreated channels and not the original selection, in order toeliminate these differences; i.e. read Collard channels as recreated Collard channels.

The noticeable differences between the new systematic error selection and the Collard selectionare within the temperature channels section, where only 17 out of 66 are the same, and in theshort wavelength region above 2000 cm−1, where very few channels are selected when taking intoaccount the systematic errors. This can be seen in figure 4.6. The reason for the disparity intemperature channels is due to not restricting the temperature selection to a particular spectralregion in the new method. Those that the Collard selection chooses in this region are sensitive towater vapour errors (as discussed previously) and hence, will not be chosen due to the systematicerrors assumed there.

In the region above 2000 cm−1, the Collard method selects several channels whereas the new

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Figure 4.5: The 252 channels selected by Collard and in the replication of Collard’s method. Thethree main spectral regions containing selected channels are shown.

Figure 4.6: The 252 channels selected by the Collard method and the new method. The threemain spectral regions containing selected channels are shown.

method picks very few. Looking at the spectral region in figure 4.1, this spectral region containsstrong contributions from both CO and N2O, on top of weak water vapour signals, which ex-plains why limited numbers are chosen there. The few that are selected are picking out strongCO2/temperature signals.

The DFS can again be compared to ascertain if more information is gathered in either method,and the DFS available for water vapour are displayed in table 4.2. The same pattern as withtemperature can be seen, in that the systematic error reduction for the Collard channels is unsur-

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Table 4.2: Degrees of Freedom for Signal available for Water Vapour from 252 temperature andwater vapour channels

Total DFS Collard Method New MethodRandom Error Only 81.19 86.48With Systematic Errors 71.79 84.47DFS per profileRandom Error Only 6.76 7.21With Systematic Errors 5.98 7.04

prisingly much larger than for the new method, although in this case the reduction is much lower.This is most likely because we have removed the most dominant error source (water vapour) andas water vapour has the largest signal in this spectral region it is not as adversely affected bythe application of the other errors. It should also be noted that after the water vapour selectionthe new method actually claims more random DFS than the Collard method, which might notbe expected due to Collard choosing to maximize this value. However, in the Collard selection,small errors due to trace gas concentrations are included within the measurement error covariancematrix and so will affect the random DFS. Whereas, these contributions are not accounted for inthe new method. They should perhaps also be included as systematic errors for completeness.

Figure 4.7: Temperature error profiles from 252 channels for the Collard Method (blue) and thenew method (red). The error due to both the random (dashed line) and systematic (solid line)errors are shown.

Figures 4.7 and 4.8 show the error profiles expected for both temperature and water vapourrespectively, having selected the 252 channels. Both cases show that the new method improvesupon the errors expected by the Collard channels, with less effect on the profile due to inclusionof the systematic errors; In the case of water vapour the profile barely changes at all between therandom profile and that with errors.

The temperature error profile appears much improved from that for 66 channels, however inthis case the error due to water vapour has not been applied due to the latter channels selectedbeing specifically chosen for water vapour, and hence that error analysis would be meaningless.

In figure 4.8 there is little to no improvement in the stratosphere from the a priori value.

19

This is most likely due to water vapour being almost entirely concentrated in the troposphere anddiminishing very quickly up into the stratosphere, therefore almost all the information availablewill be in the troposphere. The expected profile errors are perhaps fairly pessimistic, it would behoped to measure the profile to an accuracy of ∼ 10%, but this will depend upon the a prioriassumption made and in this case the assumed errors were large, forcing the majority of theinformation to come from the measurements. In a retrieval scenario a more robust a priori wouldbe used with less expected error, which could reduce these values.

Figure 4.8: Water vapour error profiles from 252 channels for the Collard Method (blue) and thenew method (red). The error due to both the random (dashed line) and systematic (solid line)errors are shown.

Ozone Channels

Having selected channels sensitive to temperature and water vapour, the quantities most neededin NWP, extra atmospheric information can be gained from including channels sensitive to tracegases (O3, CH4, CO2, CO, N2O, etc.). Subsequently 15 channels, sensitive to ozone, are chosenin the Collard selection. For this selection, only ozone is analysed; water vapour and temperatureprofiles are considered to be known. Due to time limitations, this comparison has not yet beenfully carried out.

4.3 Alternative Applications

The underlying method used for channel selection discussed in section 4.1 can be generalised tothe selection of channels for the retrieval of any species. Presented here are selections for bothmethane, CH4, and ozone, O3. In both instances a joint retrieval of the trace gas and water vapourwas carried out. This was due to the overlap in the spectrum of the features of the trace gas andwater vapour, and with the error due to water vapour being large, information in the strong peaksof the trace gas spectra would be swamped by this error. Carrying out a joint retrieval allowschannels in the major methane and ozone bands to be selected. Although it was a joint retrieval,when calculating the DFS, only the trace gas was considered, so that the channels were maximisedfor the trace gas and not for water vapour. The method used to select the channels was the sameas shown above, including the same systematic errors (CO, N2O, Non LTE effects and CH4/O3 in

20

the opposite selections), but also including an error due to the atmospheric temperature at eachlevel, calculated in the same way as those described in section 4.2.2.

4.3.1 CH4

Having carried out the selection for methane, ceasing the selection once the increase in DFS wasless than 0.5, 14 channels were obtained. These all lie within the ν4 rovibrational band surroundingthe central Q branch at 1306 cm−1. The selected channels can be seen in the top plot of figure4.9. In the lower plot; the spectra for various interfering gases within the methane band can beseen with the largest contributors being H2O and N2O.

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Figure 4.9: The 14 CH4 channels selected.

Ravazi et al. [22] choose to exclude a large section of this spectral range due to the effectsof line mixing and the interference from the water vapour continuum, despite the impact of thison the column retrieval being small. The spectral window they choose is only 1240–1290 cm−1.Wavenumbers above 1310 cm−1 are considered to be too adversely affected by the continuum. Inthis study the water vapour continuum is considered within the RFM and also the joint retrievalof methane with water vapour, hence the effects are hoped to be minimal as was seen by Crevoisieret al. [6]. The line mixing is expected to largely affect the strong Q branch but since the resultingretrieval differences are small, and the aim is to find the largest amount of information, thesechannels are not excluded here. A future comparison, could be to compare the selection shownhere to a selection limited to the same spectral range as Ravazi et al.. Crevoisier et al. also showthe large sensitivity of methane to temperature, which has been dealt with here by including it asa systematic error.

The total DFS available for methane, using the 14 selected channels, is 9.35 or 0.78 per profile.The largest contributing profile case being the mid-latitude daytime atmosphere with 1.30 DFSand the smallest being the sub-arctic atmospheres. Although slightly low, these values comparereasonably to the value of ∼ 1 quoted by Clerbaux et al. [23] and those found by Ravazi etal., who found DFS of 1.16, 1.04 and 0.92 for their considered scenes (tropical, mid-latitude andpolar regions respectively), also finding the polar regions to be have the least information. Havingapproximately 1 degree of freedom suggests that a column amount can be calculated but anaccurate profile retrieval will be difficult. In fact it is suggested that it may not even be possibleto calculate partial column retrievals [22].

21

It should also be noted that little information appears to be coming from the tropical atmo-sphere, which is surprising as the methane concentrations are expected to be higher in this region.It seems to be dramatically affected by the systematic errors included, as when considering onlyrandom errors, this reduction in comparison to the other atmospheres does not occur. It is sus-pected that this arises from the error due to atmospheric temperature and should be investigatedfurther. Although the result from this study is lower than published figures, it must be notedthat DFS depend upon the initial assumptions of the study. If the systematic errors were notconsidered, the resulting DFS would be greater.

4.3.2 O3

The ozone channel selection was carried out in the same way as that for methane, again stoppingthe selection once the increase in DFS was less than 0.5. The selected 13 channels lie in thestrong ozone band around 9.6µm. This region, 1025–1075 cm−1, also used by Boynard et al.[24], minimises the influence of interfering gases (especially water vapour). The positions of thechannels selected can be seen in figure 4.10.

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Figure 4.10: The 13 O3 channels selected

From the 13 selected channels, 21.24 DOFS are available; ∼ 1.77 per profile. Again thepolar regions contribute the least information, but this is expected as it has been shown thatinformation increases with increasing surface temperature [25]. This value is slightly lower thanmany published figures; Clerbaux et al. claim to be able to obtain vertical resolution (DOFS) of 3–4 as do Eremenko et al. [18] (although it is stated that the latter method is mainly for unusual O3

concentrations). However, Keim et al. [26], who compare several ozone inversion algorithms, showvarying numbers of DFS, from 1.5–2.8, using the LATMOS (Laboratoire Atmospheres, Milieux,Observations Spatiales, France) inversion scheme, up to 3.7 for the LPMAA (Laboratoire dePhysique Moleculaire pour l’Atmosphere et l’Astrophysique, France) inversion scheme. Againalthough the result from this study is on the low end of this variation, this could be in part dueto the initial assumptions and systematic error inclusion.

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Chapter 5

Channel Selection for Trace Gases

5.1 Impact of Stratospheric knowledge on Tropospheric Re-trievals

This chapter studies the impact of using nadir instruments, such as IASI, alongside instrumentswith different viewing geometries, and whether this can improve retrieval accuracy. The mainfocus is on tropospheric concentration retrievals.

5.1.1 Limb-viewing Instruments

The Michelson Interferometor for Passive Atmospheric Sounding, MIPAS

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was launched on March1st 2002 as one of the core instruments on board ESA’s ENVISAT satellite. It is a Fourier trans-form spectrometer used to detect atmospheric limb emission spectra in the middle and upperatmosphere (the upper troposphere, stratosphere and mesosphere) enhancing studies on atmo-spheric composition, dynamics and radiation balance [27]. MIPAS observes a wide spectral rangewithin the mid-infrared, operating from 685 cm−1–2410 cm−1 and observes the atmosphere alongtangential optical paths with an altitude range of 6–68 km. The viewing geometry can be seen infigure 5.1 and global coverage is available within 3 days. Information gained from MIPAS can beused alongside SCIAMACHY, GOMOS and MERIS, instruments also found on board ENVISAT.

Figure 5.1: MIPAS Viewing Geometry (picture by ESA)

The vertical resolution of MIPAS, ∼ 3 km, is much better than that of nadir sounders such as

23

IASI, however the observable altitude is limited to heights above clouds, and hence, troposphericknowledge is limited [28].

5.1.2 Atmospheric Sounding of Methane, CH4

Methane is a very important anthropogenic greenhouse gas, second only to Carbon Dioxide, CO2,[29]. Hence, knowledge of its emission sources and sinks is essential in the prediction of future con-centrations. Despite this, local emissions, which can be separated into natural sources (wetlands,forests, fires and termites) and anthropogenic sources (rice paddies, livestock, landfill, biomassburning and fossil fuels), are still poorly quantified [22]. Many space borne instruments give in-formation on the methane distribution, however they are usually only sensitive to certain levelsin the atmosphere. For example, limb viewing instruments such as MIPAS (the Michelson In-terferometer for Passive Atmospheric Sounding), ACE (the Atmospheric Chemistry Experiment)and HALOE (the Halogen Occultation Experiment) provide good vertical resolution but onlyin the upper troposphere and stratosphere. Nadir viewing infrared instruments, including IASI,AIRS (the Atmospheric Infrared Sounder) and SCIAMACHY have given global concentrationsbut these are limited to the mid to upper troposphere and lower stratosphere [6]. In particular,IASI’s weighting functions are sufficiently broad that the measured tropospheric concentration iscloser to a total column amount than a measurement of purely the tropospheric column amount.

Large variability can be seen in the lower troposphere and boundary layer, when close to emis-sion sources; a region that instruments are not sensitive to. This study looks at how using differentviewing instruments together may improve our knowledge of this region. Essentially, combiningthe information retrieved by IASI in the UTLS with that of MIPAS, which gives an accuratestratospheric column amount, should result in an improvement in the accuracy of troposphericcolumn amounts. Figure 5.2 shows this question in sketch form.

Figure 5.2: Areas of the atmosphere different viewing geometries can sound. MIPAS (purple)provides good vertical resolution in the stratosphere and IASI (green) is able to provide informationin the upper troposphere and lower stratosphere. Although neither instrument is able to soundthe lower troposphere well, the combination of the two sets of measurements has the potential toimprove retrievals in this region.

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5.1.3 Tropospheric Sounding

Having selected channels for the retrieval of methane, as in chapter 4, atmospheric concentrationscan be calculated. Using the 14 channels found within the 1230–1350 cm−1 spectral region theaccuracy of a tropospheric column amount retrieval can be assessed.

An initial 10% a priori error in the tropospheric column amount is assumed, where the diagonalof the a priori covariance matrix is a weighted version of the atmospheric profile and the heightlevels are assumed to be correlated. This correlation is needed due to the inability of nadir viewinginstruments to see the boundary layer and therefore to be able to calculate a total troposphericcolumn a strong a priori vertical correlation must be assumed.

The state error covariance matrix, Sx, can be calculated using equation 4.3, which shows theimprovement due to the additional use of the IASI channels on top of the initial a priori informa-tion. From this it can be investigated how an improvement in the sounding of the stratospheremay help with the retrieval of the tropospheric column amount.

A new error covariance matrix can be created, which includes an error estimate for the strato-sphere. In this case, the diagonal is again a weighted version of the atmospheric profile and theheight levels are assumed to be correlated. These values are only inserted into the stratosphericlevels of the covariance matrix, essentially assuming an infinite error in the troposphere as we aregaining no new information. In reality the new covariance would come from the output of a limbviewing instrument such as MIPAS.

This new information can be combined with the IASI information by summing the inverses,i.e.

S−1xnew = S−1

x + S−1Strat (5.1)

where SStrat is a state error covariance matrix containing new stratospheric information and Sxnew

is the new state error covariance matrix combining this additional stratospheric information withthe information from the IASI channels.

This section looks at whether varying SStrat will have any effect on the tropospheric columnretrieval. Here the tropopause is assumed to be at 12 km, therefore, if we know information above12 km, will it improve our estimate below 12 km?

Methane, CH4

Figure 5.3 shows the impact the weighted stratosphere has upon a tropospheric column amount.Both the error in the stratospheric and tropospheric column amounts can be seen as well as thetotal column amount error and the total error achieved by combining the separate stratosphericand tropospheric columns. If the troposphere and stratosphere were completely unrelated andseparate then these values would be the same, but there being a small discrepancy indicates someinteraction between the two parts of the atmosphere. In this case the discrepancy appears to bevery small but does exist and therefore some interaction is expected.

Despite this, negligible improvement is seen in the tropospheric column amount, in either therandom error case or with the inclusion of systematic errors. This seems surprising given thatwe are adding in extra information. A potential explanation could be if the averaging kernels formethane did not extend up into the stratosphere.

Figure 5.4 shows the averaging kernel for methane before any stratospheric weighting is applied.Unsurprisingly, the largest information is from the levels between 520 and 255mb; the regionsto which IASI is most sensitive. The averaging kernels are very wide and show poor verticalresolution, which is expected due to the low number of DFS available (∼ 0.78). However, they canbe seen to extend up into the stratosphere (above 200mb), indicating they should be influenced bystratospheric changes. The effect on the averaging kernels after including the weighted stratosphereis shown in figure 5.5,

As the weighting increases, to the point where we are assuming the stratosphere to be almostperfectly known, the averaging kernel sensitivity in the stratosphere is suppressed. The only levelsthat appear to be affected by the weighting are those levels above 200mb, with a slight increasein sensitivity below the tropopause towards 300mb. However this change is very small and the

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Channel Selection

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Figure 5.3: The selected methane channels and calculated column amount errors. The impact onthe tropospheric column amount due to the weighting of the stratospheric error, for both randomerrors and including systematic errors.

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Figure 5.4: The averaging kernel for Methane in a mid-latitude daytime atmosphere, with initiala priori assumptions.

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Figure 5.5: The averaging kernels for Methane with increasing stratospheric weighting. The weightis the fractional error assumed in the stratosphere.

sensitivity at lower levels is completely unaffected. This lack of change in the averaging kernelsbacks up the lack of improvement in the tropospheric column estimates. If the sensitivity doesnot change, then there is no new information to be gained from the levels.

Ozone, O3

In order to ascertain whether this negligible influence that the stratosphere has upon the tropo-sphere is limited to methane or whether it is a more general phenomenon; the same analysis hasbeen carried out for ozone. Ozone has a very different profile shape to that of methane, whichis concentrated in the UTLS, as it is almost entirely contained in the stratosphere. It would beexpected that this should mean that the stratosphere will influence the troposphere as almost allthe information is gained from the stratosphere.

The impact of weighting the stratosphere is shown in figure 5.6 which again shows the error inthe stratospheric and tropospheric column amounts as well as the total column amount error andthe total error achieved by combining the separate stratospheric and tropospheric columns.

Noticeably there is a much larger difference between the total column and the combined columnamount, suggesting more interaction between the two parts of the atmosphere. It can also be seenthat in this case there is an improvement in the tropospheric column amount as the stratosphereis weighted from 0.2 to 0.01 of ∼ 3.5%, almost halving the value. This improvement is seenwhen comparing the random error but is less noticeable with the systematic errors, where theimprovement is of only 1.5%. The promising aspect it that it does show an interaction within theatmosphere, even if not as much as expected.

The averaging kernel before weighting, shown in figure 5.7, again shows where the sensitivity toozone is within the atmosphere. Since ozone is almost entirely contained within the stratosphere,

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Channel Selection

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the averaging kernels only show sensitivity at high altitudes, and all peak above 100mb.

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The interesting result is then; how are the averaging kernels below the tropopause affected asthe stratosphere is suppressed? Figure 5.8 shows the progression as the stratosphere is increasinglyweighted. A larger change than that observed by methane is seen here. Similarly, the main changeis below the tropopause and this increases as the weighting increases, but due to the shape of theaveraging kernels the effect spreads further down into the troposphere. The averaging kernels canbe seen to become more sensitive in the troposphere, which corresponds to the improvement inthe tropospheric column amount. A point to note is that the signal for ozone is much larger thanthat of methane, with almost twice the amount of information (DFS) available, and this couldpotentially contribute to the difference observed.

Future Work

The work contained in this chapter is ongoing and I would hesitate to say currently that theresult is the truth. The surprisingly small effect on methane leads me to suspect something in myassumptions or method is incorrect. However, the example using Ozone does appear to back upthe result. I believe that i will need to investigate further before a full conclusion is made.

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Chapter 6

Future Work and Thesis Plan

Selecting increasing numbers of channels, and the effect of this upon the expected column amounterrors was also investigated in my first year, extending the error analysis to a profile retrieval incertain cases. The need for highly accurate model parameters in order to retrieve the columnamount of a gas to high accuracy was highlighted. Principle Component Analysis was also brieflyexplored for its ability to capture the modes of variability in IASI data.

My second year of study has been described in this report. The research that I plan to carryout in the following year, which will complete the detailed research chapters of my final thesis,will include the following topics:

Thesis Chapters

1. Introduction

(To be written once thesis is more complete) Introducing the thesis and its contents, includingsections covering the following topics:

The Infrared Atmospheric Sounding Interferometer (IASI)

A detailed description of the IASI instrument, the instrument my thesis is based upon. Detailingthe main characteristics and uses of the instrument. (A description has been included in both firstand second year reports, this will form the basis of th but will be expanded on to include moredetail)

Theory

A section to contain the background on Optimal Estimation and the main concepts used through-out the thesis. (Parts of theory already written and included with first and second year reports.)

2. Forward Model Comparison

Radiative Transfer models are used to simulate observations and are a fundamental part in theretrieval of atmospheric compounds. There are two main types of radiative transfer models: lineby line radiative transfer models and fast radiative transfer models. Although line by line modelsproduce much more accurate results they are very computer processing heavy and cannot be run innear real time. For this reason, fast radiative transfer models were developed for use in numericalweather prediction. Descriptions of RTTOV (a fast radiative transfer model) and the RFM (aline by line model) were included in my 1st year report. These descriptions will be expandedon and a comparison of the two models will be carried out. (Comparison to be done during 3rdyear.) This year my work has been solely with the RFM, however next year I plan to extend

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my investigations into using the RTTOVs model as well. Due to the different ways the modelsformulate the radiances, the outputs are different. I plan to compare the produced spectra andattempt to clarify where in the modeling any discrepancies arise. Obviously differences occur dueto the different methods used but my aim is to see exactly which parts causes the greatest changes,for example, whether the differences are due to the line mixing ratios or the line shape. Both modelswill also be compared to the Met Office forecast model and see which forward model comparesmost favourably to it. Currently the set of predictors used by RTTOVs in the transmittancecalculation are created using the LBLRTM (Line by Line Radiative Transfer Model). I plan toset up the RFM to calculate these predictors and compare the outputs to those by the LBLRTM.Also to compare the outputs once both predictor sets have been run through RTTOVs. This willhopefully be of use to the Met Office as currently they rely on only one model.

3. Channel Selection

This chapter will contain the work shown in chapters 4 and 5 of this report. A description ofthe methods considered for selecting an optimal set of channels for use with IASI for NumericalWeather Prediction and also for trace gas retrievals. Validation of retrievals using my selectedchannels in the Met Office 1D-Var retrieval scheme will also be included. The validation will becarried out during my third year of study.

4. Atmospheric Composition using IASI

The final major chapter of my thesis will contain full 1D retrievals using IASI data. Using thechannels selecting with the method established in the previous chapter, the atmospheric compo-sition of the atmosphere will be looked at, concentrating on temperature, water vapour, ozoneand methane. The retrievals from the new nadir version of the Oxford MORSE (MIPAS OrbitalRetrieval using Sequential Estimation) code and the Met Office 1D-Var retrieval code will becompared and the residuals examined to try to ascertain where any problems lie.

5. Conclusion

The conclusion will summarise the work and results contained in my thesis and the scientificimprovements that have been made.

Prelimenary work that may be included

One of the essential inputs into radiative transfer models is a reliable spectroscopic databasethat contains accurate line shapes and positions as well as many other active atmospheric gasparameters. There are several databases to choose from including the HIgh-resolution TRANs-mission molecular absorption database (HITRAN) and Gestion et Etude des Informations Spec-troscopiques Atmospheriques (GEISA) databases. These are updated regularly as new research iscarried out, and during my first year the impact of these updates was assessed. This work will notdefinitely be included, however, if the differences between the forward models or the differencesbetween retrieval methods appear to be due to spectoscopic differences, the work may be includedwithin one of those chapters.

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Timeline

October – December

Chapter 3 : Channel SelectionThe channel selection method will be completed, with the addition of emissivity as an error

source and the validation of the channel selections will be carried out. Once the validation hasbeen completed, I aim to write up my channel selection work and method into a paper, which cansubsequently be written into the thesis chapter.

January – March

Chapter 2 : Forward Model ComparisonThe work that will be contained within chapter 2 will be carried out and once completed it

will be written up into the thesis chapter.

April – June

Chapter 4 : Atmospheric Composition using IASII will begin to use real IASI data to compare retrievals using my selected channels. Again,

once completed, the work will be written up into the thesis chapter.

July – September

I will continue to write up the remaining parts of the thesis and complete any chapters I am behindon writing up.

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