Atmospheric sounding using IASI 2 nd Year Report words. Lucy Ventress AOPP, University of Oxford

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1 Atmospheric sounding using IASI 2 nd Year Report words Lucy Ventress AOPP, University of Oxford August, 2011

2 Contents 1 Introduction 2 2 The Infrared Atmospheric Sounding Interferometer 4 3 Theory Background Optimal Estimation The Averaging Kernel The Forward Model Figures of Merit Column amount Channel Selection for use in Numerical Weather Prediction Channel selection methods Main selection algorithm The Collard Method Including Systematic Error Example - Replication and comparison Replication Systematic Error Calculation Comparison Alternative Applications CH O Channel Selection for Trace Gases Impact of Stratospheric knowledge on Tropospheric Retrievals Limb-viewing Instruments Atmospheric Sounding of Methane, CH Tropospheric Sounding Future Work and Thesis Plan 30 1

3 Chapter 1 Introduction Atmospheric observations are vital in the process of weather forecasting. (It is only with a large range of observational data monitoring the Earth s atmosphere, land and ocean that forecasts can be produced.) Presently observations come from several sources: radiosondes, surface data, aircraft observations and satellites. Satellites have become the prevalent source of data due to the 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 Numerical Weather Prediction windows (typically 6hrs for global NWP). To overcome this problem the process of data assimilation is used. This involves combining the observations available with a forecast of what the conditions are expected to be to form a best estimate of the current state of the atmosphere. This best estimate, or analysis, is the starting point for numerical weather forecasts, which simulate the Earth s atmospheric processes that can affect the weather and it is this 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. A large number of weather centres now use the spectral data from IASI and its use has been shown to have a significant positive impact on global Numerical Weather Prediction (NWP). This impact became apparent shortly after IASI s launch despite its data initially having a fairly conservative weighting during the assimilation process [1]. Its absolute radiometric accuracy has been shown through comparisons with data from AIRS (Atmospheric InfraRed Sounder) and the 11 micron AATSR (Advanced Along-Track Scanning Radiometer) channel, in both cases agreement being better than 0.1 K, [2] [3]. IASI measures the radiance emitted from the Earth and atmosphere. In a cloud free region the radiance can be measured throughout the atmosphere. This allows vertical information to be derived for certain species, assuming surface parameters such as temperature and emissivity and the atmospheric temperature are known or simultaneously retrieved. The large number of channels available from the IASI instrument leads to a very large quantity of data. However, this can lead to problems within retrievals and data assimilation. Choosing an optimal subset of the channels is an established method to reduce the amount of data, whilst maintaining the information contained within it [4]. In selection, the amount of information retrieved needs to be optimised whilst also eliminating spectral regions with large uncertainties. The method currently used at the UK Met Office to select their channels is discussed in chapter 4 along with an alternative method with potential 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 wave CO 2 band and provide atmospheric temperature information, whilst the IASI water vapour band is currently under used. Only a fews tens of the water vapour channels are assimilated. Reasons suggested for this limited use include difficulties with the observation error correlations and biases in the assimilating NWP models [1]. 2

4 Principal Component Analysis (PCA) is a method of projecting spectra onto a principal component 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 filtering whilst minimizing the loss of information on trace gases and species, and a strong compression rate [5]. The compressed PC spectra are disseminated in parallel with the full IASI radiance spectra. However, attention must be paid to ensure that all relevant information is kept and also that certain events, such as biomass burning or pollutant plumes, are not missed. This depends upon similar features being included in the training set of spectra used to create the PCs and the number of PCs chosen for compression. It remains to be investigated whether or not trace gas retrievals can benefit from the increased signal to noise ratio in the reconstructed radiances and if the PC scores will contain enough information for applications such as chemistry monitoring or if the full IASI spectra will be needed. Although investigated briefly as an alternative method, PCA is not the focus of this report. The primary instrument in my project is IASI, a nadir viewing instrument. Chapter 5 considers the impact of combining measurements from nadir instruments, such as IASI, alongside instruments with different viewing geometries, and whether this can improve retrievalaccuracy. For example, limb viewing instruments such as MIPAS (the Michelson Interferometer for Passive Atmospheric Sounding) provide good vertical resolution but only in the upper troposphere and stratosphere. Nadir viewing infrared instruments, including IASI, have given global concentrations 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 emission sources. This study looks at how the potential of using different viewing instruments together may 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, primarily investigating methods of channel selection and how these methods can be improved upon. 3

5 Chapter 2 The Infrared Atmospheric Sounding Interferometer The Infrared Atmospheric Sounding Interferometer (IASI) is one of eleven instruments on board the METOP-A satellite that was launched on 19 th October It was developed by CNES (Centre National d Etudes Spatiales) for use as part of the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) European Polar system. Two more instruments in the same series of satellites are planned to be mounted on METOP-B and METOP-C, due to be launched in 2012 and 2016 respectively, allowing the IASI instrument to provide observations over a long timescale. The IASI instrument is an infrared Fourier transform spectrometer associated with an imaging system, which uses nadir viewing geometry passively to measure the radiance from Earth. The radiation emitted from both Earth and the atmosphere is affected by the emission, absorption and scattering of the atmospheric molecules along its optical path. It is the radiation resulting from these interactions that IASI measures as the atmospheric spectrum, containing atmospheric emission/absorption features [7]. The instrument is based on a Michelson interferometer: this decomposes the emitted spectrum, after which an inverse Fourier transform and radiometric calibration 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 detail elsewhere, ([8] [9] [10] [11]), but include: Ascanningmirror. An afocal telescope to transfer the image onto the scan mirror. The Michelson interferometer, including a beam splitter and corner cube mirrors that are designed to create the optical path difference necessary for the specified spectral resolution. The cold box, which the recombined beams are directed into, containing refractive optics that divides the spectral range into three bands. The Digital Processing Subsystem that carries out the inverse Fourier transform and calibration on the interferograms before being transmitted to the ground. The main characteristics of the IASI instrument are summarised in Table 2.1. IASI covers the spectral range cm 1 with a spectral resolution of 0.25 cm 1 (0.5cm 1 apodized) giving a total of 8461 channels in each IASI spectrum. This results in a large quantity of data that can cause problems for both retrievals and data assimilation. Choosing an optimal subset of the channels is an established method to reduce the amount of data and is discussed in chapter 4. The spectral range is divided into three bands as shown in figure 2.3. Band 1, from cm 1, is primarily for temperature and ozone sounding, Band 2, from cm 1, is primarily 4

6 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 N2 O and CH4 column amounts and Band 3, from cm 1, is for temperature sounding and the retrieval of N2 O and CO column amounts [13]. IASI achieves global coverage and provides measurements twice a day at each location, at a local time of 09:30, from a sun synchronous orbit. The instrument scans perpendicularly to the motion of the satellite between angles and , with respect to the nadir. Within each scan there are 30 steps at which measurements are taken, as well as views to the calibration targets: an internal hot black body and cold deep space. Each view consists of a 2 2 matrix containing independent circular pixels with diameters of 12 km, as shown in figures 2.4a and 2.4b, to increase the probability of obtaining cloud free views. The IASI scan pattern provides measurement locations compatible with other instruments on board the METOP satellite [9]. 5

7 Table 2.1: IASI Instrumental Characteristics (taken from Clerbaux et al. [12]) Spectral Range Spectral Resolution Instrumental Noise Pixel Size Data Rate Lifetime Altitude Orbit Inclination Local time Orbital Period Characteristics cm 1 ( µm) 0.5 cm 1 (apodized) 0.2 to 0.35 K (NEDT at 280 K) Diameter of 12 km, 4 pixels matrix, across-track scanning 1.5 megabits per second 5 years 817 km polar sun-synchronous 98.7 to the equator 09: 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

8 Chapter 3 Theory 3.1 Background In this chapter the theoretical basis of the work contained in this report is discussed Optimal Estimation This study was based on the use of optimal estimation as described by Rodgers, 2000 [14]. Here the notation and definitions are briefly explained. The atmospheric profile, in terms of temperature or the concentration of a particular atmospheric molecule, at a given location, is represented by the state vector x. The satellite measurements, i.e. brightness temperature spectra, are represented by the vector y. These two vectors can be related by the following equation: y = F(x)+ɛ, (3.1) where ɛ is a combination of the measurement and forward model errors whose resulting error covariance matrix is given by S y, and F(x) is the forward model function that contains our understanding of the physics of the situation. Assuming the radiative-transfer equation to be weakly non-linear, this can be linearised around a reference state, x 0,becoming y F(x 0 )= F(x) x (x x 0)+ɛ = K(x x 0 )+ɛ (3.2) where K is known as the weighting function matrix, or Jacobian matrix and contains the partial derivatives of each forward model element with respect to each state vector element, i.e. K ij = y i x j. (3.3) The inverse problem is concerned with retrieving an estimate of x from gathered measurements, y. Remote sensing is usually an ill-posed problem and cannot be entirely defined by the measurement, hence, optimal estimation includes the use of apriorivalues, [7]. These can be any previously known information about the atmospheric conditions that is unrelated to the measurements taken, such as climatology. In optimal estimation a solution is found that minimises the square of the differences between both the measurements and the forward model and the apriori values with the estimate of the state variable. i.e. χ 2 =(y Kx) T S 1 y (y Kx)+(x a) T S 1 a (x a) (3.4) is minimised, where a is the aprioriestimate of the solution and S a and S y are the apriori and measurement/forward model error covariance matrices respectively. If the inverse apriori 7

9 error covariance matrix, S 1 a, 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 weighted least squares fit retrieval. Once minimised, in the linear limit, equation 3.4 allows the retrieved state vector to be expressed as x =(K T S 1 y which can also be written in the form, in terms of the gain matrices K + S 1 a ) 1 (K T S 1 y + S 1 a), (3.5) y a x = Gy + Ha, (3.6) G = (K T S 1 y H = (K T S 1 y = Gy +(I n GK)a (3.7) K + S 1 a K + S 1 a ) 1 K T S 1 y, (3.8) ) 1 S 1 a, (3.9) where G is how the retrieval depends on the measurements and H is how the retrieval depends upon the apriori.the assumption that the measurements and aprioriare uncorrelated with each other is made allowing the corresponding state vector error covariance matrix, S x, to be written as S x = GS y G T + HS a H T, (3.10) which reduces to, The Averaging Kernel S x =(K T S 1 y K + S 1 a ) 1. (3.11) The averaging kernel matrix, A, relates the sensitivity of the retrieval to the true state. Its elements are defined to be A ij = dx i dx t, (3.12) j where x t is the true value of the state. This arises from the substitution of y = Kx t into equation 3.6, x = GKx t + Ha. (3.13) It can be shown that GK + H = I (where I is the identity matrix), which allows x to be expressed as x = GKx t +(I GK)a, (3.14) = Ax t +(I A)a, (3.15) and A to be defined as A = GK. (3.16) The Forward Model Radiative transfer models attempt to calculate accurate top of atmosphere radiances, given atmospheric 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. This is convolved with the channel s corresponding spectral response function to obtain the simulated radiance expected from a particular instrument. There are two main types of radiative transfer models: line by line radiative transfer models and fast radiative transfer models. Although line by line models produce much more accurate results they are very computer processing heavy and cannot be run in near real time. For this reason, fast radiative transfer models were developed for use in numerical weather prediction. 8

10 The Reference Forward Model, or RFM, is a line by line model developed at the University of Oxford [15]. It is based on the line by line model GENLN2 [16] and was developed originally for use with the Michelson Interferometer for Passive Atmospheric Sounding, MIPAS, but has since been developed into a general purpose code for many spectroscopiccalculations. Linebyline models perform accurate calculations of atmospheric radiances and transmittance at high spectral resolution. The RFM is the forward model used in this report to simulate the atmospheric spectra and create the Jacobian matrix, K, which in turn is used to calculate the error covariance matrix of the state vector as shown in section In the case of atmospheric gases, the Jacobian calculated by the RFM is the rate of change of the brightness temperature spectrum to a 1% perturbation of the state vector, i.e. K = δt b δx[%] =T b(x[ppm] 1.01) T b (x[ppm]) (3.17) where the state vector can either be the total column amount of a particular gas or the concentration of a gas at different levels in the atmosphere, and hence, when running the forward model the perturbation can be applied to the column as a whole or to each individual layer (of specified thickness) respectively. When calculating the Jacobian for atmospheric temperature, the rate of change due to a 1 K perturbation to the state vector is calculated Figures of Merit A figure of merit is a quantity that characterises performance: for example, how much information can be gained by the additional use of a measurement channel. Information in the retrieval error covariance matrix is compressed into a single scalar quantity allowing the intercomparison of different covariance matrices (e.g. from using different measurement channels). Two figures of merit generally used are the Shannon information content and the degrees of freedom for signal (DFS). The former is defined (e.g. Rodgers, [14]) to be the factor by which knowledge of a quantity is improved by making the measurement and aims to improve the error analysis at any point in the profile regardless of previous knowledge at that level: H = 1 2 ln I n A = 1 2 ln S x S 1 a, (3.18) where I n is the identity matrix and A is the averaging kernel matrix. Alternatively, the the degrees of freedom for signal, d s can be used, which is essentially the number of independent pieces of information you have in an estimate of a measurement. It attempts to spread the information over the profile, favouring levels where there is a lack of knowledge. d s = tr([k T S 1 y K + S 1 a ] 1 K T S 1 y 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 so that the profle as a whole is improved and not just certain levels 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 the Earth s surface to the top of the atmosphere. It can be defined as: 0 x(z) ρdz, (3.22) 9

11 where x(z) is the state vector as a function of height, z and ρ is the air density. When discussing the 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 temperature and pressure and gives the conversion 1DU = molecules cm 2. The Dobson unit is used throughout this report when referring to the column amount. The RFM produces the rate of change of the brightness temperature to a 1% perturbation of the state vector as described in section 3.1.3, where the state vector has the units of ppm (parts per million). The conversion from ppm to Dobson s units is as follows: c[du] = = p 0 0 x[ppm] ρ air dz (3.23) x[ppm] g dp, (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 extended trapezoidal rule is implemented, i.e. p 1 p n 1 x(p) dp p[ 2 x 1 + x x n x n], (3.25) where n is the number of levels in the atmosphere and p is the height (in pressure units) of each layer. However, when integrating over pressure co-ordinates, the heights of each of the layers are not equal and the integral becomes: p n p 1 x(p) dp n ( ) xi + x i+1 (p i+1 p i ). (3.26) 2 i=1 Following from the above equations, an alternative way of calculating the column amount, c, and its associated covariance error, S c, is to express them as c = a 1 x 1 + a 2 x a n x n (3.27) = a T x (3.28) S c = σ 2 c (3.29) = a T S x a (3.30) where the a i values are constants that represent the transformation from the concentrations in different layers to column amount. There are two possible forms of the state vector; a column amount value or a vertical profile. If working directly with the whole atmosphere (a column amount state vector), the calculated error covariance, S x, is a scalar and equals the column amount error covariance, i.e. S x =S c and hence the total column amount error is purely the square root of this value, see equation However, when working with an atmospheric profile the error covariance must be first transformed into a total column amount covariance error using equation

12 Chapter 4 Channel Selection for use in Numerical Weather Prediction 4.1 Channel selection methods The large number of channels available from the IASI instrument leads to a very large amount of data. Choosing an optimal subset of the channels is an established method to reduce the amount of data, [4]. In this section methods for the selection of this subset are discussed and compared Main selection algorithm The channel selection method used by Collard [4] was based on that given by Rodgers [14] and was shown by Rabier et al. [17] to be the best selection method for the IASI instrument despite the expense in terms of computer processing time. The selection method is based on linear optimal estimation theory for use in Numerical Weather Prediction (NWP), which was discussed is chapter It was shown that the state error covariance matrix, S x can be written as, S x =(K T S 1 y K + S 1 a ) 1. (4.1) The iterative method calculates the impact of each channel upon a figure of merit, assessing how much information can be gained by the additional use of a measurement channel. The figure of merit used in this study is the degrees of freedom for signal (DFS) given by, d s =tr(i n S x S 1 a ) (4.2) which attempts to spread the information over the profile, favouring levels where there is a lack of knowledge. The method proceeds as follows: 1. Begin with an assumption of S a,e.g.aforecastmodelcovariance. 2. S x and subsequently the DFS can be calculated individually for each channel and that with the largest DFS is selected. 3. S a is updated to contain the information gained from the previously selected channel(s), i.e S a = S x. 4. The process is repeated from step 2 selecting the next best channel from those remaining that 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

13 A variant on this method suggested by Rabier et al., 2002 [17] that decreases the processing time is in principle the same but does not update the error covariance matrix for each selection throughout the analysis. Therefore, any selection made does not take into account the information gained from previously selected channels. Despite the increase in speed of selection given by this variant, during this study the method initially described was the method chosen to use in order to achieve the greatest accuracy The Collard Method In the method used by Collard [4], before using the main slection algorithm described in section 4.1.1, channels are removed based on certain criteria in an effort to avoid spectral regions with large uncertainties. There are several pre-screening approaches considered, although not all are used. Channels that are stated to be avoided are those highly sensitive to radiative-transfer model errors or species whose variability is not considered in the background or analysis, or have known radiative transfer weaknesses. In reality, this means that a channel is blacklisted if the effect on the brightness temperature due to the climatological variability of a trace gas is greater than 1 K. In the example given by Collard this includes CH 4,COandN 2 O. If the impact was less than 1 K then the effect of the trace gas is added to the forward-model error covariance matrix. Channels affected by solar radiation (λ < 5 µm) are also blacklisted so as the channels selected are not limited to use solely at nightime Including Systematic Error In the new method described in this study, an effort is made to eliminate the need for ad hoc prescreening and instead attempts to include the systematic errors due to the trace gases within the selection process. Taking the total state error covariance matrix to be a combination of the random error component, given by Eq. 3.11, and a systematic error component, containing contributions from influences such as trace gases and non thermal equilibrium (NTE) effects, we obtain S TOT x = S RND x + S SY S x (4.3) where S RND x =(K T S 1 y K + S 1 ) 1 and the systematic component is given by a S SY S x = i (δx SY Si )(δx SY Si ) T. (4.4) This is similar to the error method used by Eremenko et al. in calculating the total errors for an Ozone retrieval [18]. Mathematically, the change in the state vector due to a systematic error is δx SY Si = Gδy SY Si SY Si +(I n GK)δxa (4.5) SY Si from Eq In the first instance the aprioriis assumed to have no systematic errors: δxa is assumed to be zero. However, once in the iterative part of the selection algorithm it is updated SY Si each time with the change in state vector due to the errors, i.e. δxa = δx SY Si, in a similar fashion to the update of the apriorirandomerrorcovariance matrix to contain the new information gained from the selected channel. In this method the apriorierrorcovariance matrix becomes the random component of the state error covariance matrix, i.e. S RND a = S RND x each iteration. The measurement change due to the effect of systematic errors, δy SY Si, is the brightness temperature difference between spectra produced from a profile perturbed by the atmospheric variability of the species and an unperturbed profile calculated using the RFM. 12

14 4.2 Example - Replication and comparison In order to compare the current selection with the proposed improved method, the practical selection of 300 channels carried out by Collard has been recreated and subsequently carried out with the new method. It should be noted that there are some small differences between the original 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 fast Radiative Transfer model for IASI ([19], [13]), also the background error covariance matrix will differ even though both are based upon operational 1 D-Var matrices. It is thought that these will not 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 by Collard Replication The following example shows the selection of 300 channels for distribution to NWP centres in near real time. This means that the only species of interest in the channels selected are water vapour, ozone, carbon dioxide and temperature. The same channel flags used by Collard (personal communication) are used to blacklist the channels whose climatological variability cause a brightness temperature change of > 1 K. For those whose impact was greater than 1 K, CH 4,CO and N 2 O, 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 1 K. Their effect was instead added to the combined measurement and forward model error covariance matrix. Here the impacts of the trace gases to be included in this manner, CCl 4,CFC-11,CFC-12,CFC-14, HNO 3,NO 2,OCS,NOandSO 2,arecalculatedusingthesixstandardFASCODEatmospheres (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 space and the instrument noise is the CNES estimate post launch from 2008 (Hilton, personal communication). 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 Office 1 D-Var IASI background and the forward model used for the calculation of the Jacobians is the RFM. 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 as generalised as possible and not specific to one atmospheric scene. Finally, the interferograms produced by IASI are apodized to suppresses sidelobes which would otherwise be produced. However, this is at the expense of broadening the central peak and therefore 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 data has a spectral response function of width 0.5cm 1. This means that once a channel is selected the neighbouring channels are flagged and cannot also be selected Systematic Error Calculation The systematic error contributions from the absorbing gases, CH 4,CO,O 3 and N 2 Oarecalculated as the difference in brightness temperature between spectra calculated from a profile perturbed by the atmospheric variability of the absorber and an unperturbed profile. The atmospheric variabilities were calculated in 2001 for use with MIPAS (Michelson Interferometer for Passive Atmospheric Sounding). The error contribution from Non-LTE effects was calculated from the difference between spectra including the effect and without. 13

15 280 Brightness Temperature, K H2O CO2 O3 CO CH4 N2O Wavenumber, cm -1 Figure 4.1: Atmospheric spectra for species whose atmospheric variability cause a brightness temperature change of > 1 K 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 CO 2 spectrum) it can be seen that the largest influences will be from water vapour and ozone, whereas when 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 B matrix used in the Met Office 1D-Var model, available from NWPSAF (the EUMETSAT network of Satellite Application Facilities for Numerical Weather Prediction)[21]. If the assumption is made that all the variability of water vapour is correlated in height through the atmosphere then only one perturbation, consisting of the roots of the diagonal elements of the background matrix, need be considered. However, for the most part this is not the case and would cause an over estimation of the error contribution. Instead 27 separate systematic errors are used. The B matrix, containing values for the lowest 26 of the 1D-Var s 43 atmospheric levels (up to 120 mb), is decomposed into 26 independent contributions, in other words its eigenvectors. The perturbations applied are each 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 the troposphere and that errors above this point can be assumed to be uncorrelated and approximately 20% of the total concentration at each height Comparison Temperature channels Initially, only a temperature analysis is considered. In the Collard method all potential blacklisted channels are excluded whereas in the new method the errors from these absorbers, including water vapour and ozone, are included as systematic errors. By initially only retrieving temperature, this attempts to ensure that the temperature information gained comes from CO 2 channels and not from H 2 O channels. Thirty channels are selected in this manner. Subsequently in the Collard method, a further 36 channels are selected having restricted the possible channels to the range cm 1, with the intention of improving the information in the troposphere as this has 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

16 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 troposphere might be preferentially selected, whilst allowing a channel whose Jacobian did not peak in this region to still be selected should its information be significantly greater. Having included this weighting, to various degrees, it appeared that it madelittlesignificantdifferencetotheerror profile achieved by the different channel selection. This can be seen in figure 4.3, which shows the random error of a temperature profile using the sets of channels selected using different weightings; the tropospheric improvement is of only 0.01 K. Therefore, it was deemed unnecessary to include it in the new method. The difference between the channels selected by Collard and those selected in 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 appear to capture the same information. Figure 4.4 shows the error profiles of the first 66 channels selected in both Collard s method and the new method including systematic errors. Both the random errors and the systematic errors are shown. It can be seen that the Collard channels do have much smaller random errors in the troposphere, which is essentially why they were chosen. However upon including the systematic errors with these channels, the errors are significantly increased, with those in the stratosphere little affected. Likewise, the channels selected with the errors included are little changed in the stratosphere but again have a fairly large change in the troposphere when comparing to the random error. It is also interesting to compare the DFS for each profile as this tells how many pieces of actual information we can get. The total number of Degrees of Freedom available, which has been maximised in the selection, is for 6 atmospheres and 2 viewing angles. Therefore, the total available per profile is a twelfth of this value. Table 4.1 shows that when comparing the random error component (essentially only the radiometric noise) then both selection methods obtained a very similar value. The Collard method has a slightly higher DFS, which is expected as this is the quantity that was maximised for that selection. However, the fact that both give a similar value shows that there are likely to be several combinations of channels that will provide the same quantity of temperature information. The difference comes from the effect of interfering gases in these channels. Calculating the effect of these systematic errors has little effect upon the DFS for the new method as this is the value maximised for that selection. This is due to the selection choosing channels with very small sys- 15

17 Figure 4.3: Temperature error profiles for 66 channels. different weightings on the troposphere. All show the random error and have Figure 4.4: Temperature error profiles from 66 channels for the Collard Method (blue) and the new method (red). The errorprofiles due to both the random error (dashed line) and the combined systematic and random errors (solid line) are shown. tematic errors and therefore the DFS for that channel will be very close to the random DFS for that channel. However, although the Collard method may choose channels with a slightly better random component, once the systematic errors are included the DFS are drastically reduced by more than a factor of 2. This implies that the channels selected in the new method should be able to provide the same amount of random information as those currently used but with less sensitivity to errors caused by trace gases. 16

18 Table 4.1: Degrees of Freedom for Signal available from 66 temperature channels Total DFS Collard Method New Method Random Error Only With Systematic Errors Without Water Vapour Error DFS per profile Random Error Only With Systematic Errors Without Water Vapour Error A large increase in error in the upper troposphere in the Collard error channels can be seen in figure 4.4 when including the systematic errors. This is expected to be due to one or two of the channels being particularly sensitive to water vapour, an error included in the selection of the other set of channels. This can also be seen in the DFS available if the water vapour error is included and if it is not, and respectively. This implies that the Collard channels have a sensitivity to water vapour, but as the main retrieval will also be for water vapour this is not necessarily a bad thing. Main Channels Once the temperature channels have been selected, the main run is carried out. This consists of a joint retrieval of both water vapour and temperature. In the Collard case, the water vapour channels are un-blacklisted and are now available for selection. The joint retrieval is so that although the channels selected will mainly be sensitive to water vapour they will also provide additional temperature information, on top of that already gained in the first 66 channels. The new 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, giving a total of 252 channels. The water vapour apriorivalues come from the Met Office 1D-Var B matrix. Although provided on the Met Office pressure grid, the water vapour only extends to the 26th level ( 120mb). For the height levels above this, the apriorierrorswere calculated from the standard FASCODE atmospheres and assumed to be uncorrelated. Firstly, the replication of the Collard selection was carried out and figure 4.5 shows which channels were selected. It can be seen, in green, that many of the channels are common to both the replication (purple) and the original collard selection (orange), especially amongst the temperature region in the top panel, where 46 out of the 66 selected temperature channels are the same. The discrepancy in channels appears to be mainly in the selection for water vapour in the lower panels, although despite not selecting the identical channels, very similar or neighbouring channels are chosen. It is expected that these differencesariseduetothesubtleunavoidable differences in the replication, such as the forward model used and the aprioriassumptions. Since these factors will not vary between the new systematic error method and the replication, from this point all comparisons are to the recreated channels and not the original selection, in order to eliminate these differences; i.e. read Collard channels as recreated Collard channels. The noticeable differences between the new systematic error selection and the Collard selection are within the temperature channels section, where only 17 out of 66 are the same, and in the short wavelength region above 2000 cm 1,whereveryfewchannelsareselectedwhentakinginto account the systematic errors. This can be seen in figure 4.6. The reason for the disparity in temperature channels is due to not restricting the temperature selection to a particular spectral region in the new method. Those that the Collard selection chooses in this region are sensitive to water vapour errors (as discussed previously) and hence, will not be chosen due to the systematic errors assumed there. In the region above 2000 cm 1,theCollardmethodselectsseveral channels whereas the new 17

19 Brightness Temperature, K Collard only Recreation only Both Wavenumber, cm -1 Brightness Temperature, K Wavenumber, cm -1 Brightness Temperature, K Wavenumber, cm -1 Figure 4.5: The 252 channels selected by Collard and in the replication of Collard s method. The three main spectral regions containing selected channels are shown. Figure 4.6: The 252 channels selected by the Collard method and the new method. The three main spectral regions containing selected channels are shown. method picks very few. Looking at the spectral region in figure 4.1, this spectral region contains strong contributions from both CO and N 2 O, on top of weak water vapour signals, which explains why limited numbers are chosen there. The few that are selected are picking out strong CO 2 /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 with temperature can be seen, in that the systematic error reduction for the Collard channels is unsur- 18

20 Table 4.2: Degrees of Freedom for Signal available for Water Vapour from 252 temperature and water vapour channels Total DFS Collard Method New Method Random Error Only With Systematic Errors DFS per profile Random Error Only With Systematic Errors 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) and as water vapour has the largest signal in this spectral region it is not as adversely affected by the application of the other errors. It should also be noted that after the water vapour selection the new method actually claims more random DFS than the Collard method, which might not be 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 covariance matrix and so will affect the random DFS. Whereas, these contributions are not accounted for in the 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 the new 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 vapour respectively, having selected the 252 channels. Both cases show that the new method improves upon the errors expected by the Collard channels, with less effect on the profile due to inclusion of the systematic errors; In the case of water vapour the profile barely changes at all between the random profile and that with errors. The temperature error profile appears much improved from that for 66 channels, however in this case the error due to water vapour has not been applied due to the latter channels selected being 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 apriorivalue. 19

21 This is most likely due to water vapour being almost entirely concentrated in the troposphere and diminishing very quickly up into the stratosphere, therefore almost all the information available will be in the troposphere. The expected profile errors are perhaps fairly pessimistic, it would be hoped to measure the profile to an accuracy of 10%, but this will depend upon the apriori assumption made and in this case the assumed errors were large, forcing the majority of the information to come from the measurements. In a retrieval scenario a more robust aprioriwould be 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 the new 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 needed in NWP, extra atmospheric information can be gained from including channels sensitive to trace gases (O 3,CH 4,CO 2,CO,N 2 O, etc.). Subsequently 15 channels, sensitive to ozone, are chosen in the Collard selection. For this selection, only ozone is analysed; water vapour and temperature profiles are considered to be known. Due to time limitations, this comparison has not yet been fully carried out. 4.3 Alternative Applications The underlying method used for channel selection discussed in section 4.1 can be generalised to the selection of channels for the retrieval of any species. Presented here are selections for both methane, CH 4,andozone,O 3. In both instances a joint retrieval of the trace gas and water vapour was carried out. This was due to the overlap in the spectrum of the features of the trace gas and water vapour, and with the error due to water vapour being large, information in the strong peaks of the trace gas spectra would be swamped by this error. Carrying out a joint retrieval allows channels 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 maximised for the trace gas and not for water vapour. The method used to select the channels was the same as shown above, including the same systematic errors (CO, N 2 O, Non LTE effects and CH 4 /O 3 in 20

22 the opposite selections), but also including an error due to the atmospheric temperature at each level, calculated in the same way as those described in section CH 4 Having carried out the selection for methane, ceasing the selection once the increase in DFS was less than 0.5, 14 channels were obtained. These all lie within the ν 4 rovibrational band surrounding the central Q branch at 1306 cm 1. The selected channels can be seen in the top plot of figure 4.9. In the lower plot; the spectra for various interfering gases within the methane band can be seen with the largest contributors being H 2 OandN 2 O. delta T, K Brightness Temperature, K O3 CO H2O N2O CH Wavenumber, cm -1 Figure 4.9: The 14 CH 4 channels selected. Ravazi et al. [22] choose to exclude a large section of thisspectralrangeduetotheeffects of line mixing and the interference from the water vapour continuum, despite the impact of this on the column retrieval being small. The spectral window they choose is only cm 1. Wavenumbers above 1310 cm 1 are considered to be too adversely affected by the continuum. In this study the water vapour continuum is considered within the RFM and also the joint retrieval of methane with water vapour, hence the effects are hoped to be minimal as was seen by Crevoisier et al. [6]. The line mixing is expected to largely affect the strong Q branch but since the resulting retrieval differences are small, and the aim is to find the largest amount of information, these channels are not excluded here. A future comparison, could be to compare the selection shown here to a selection limited to the samespectralrangeasravaziet al.. Crevoisieret al. also show the large sensitivity of methane to temperature, which has been dealt with here by including it as a 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 DFS and the smallest being the sub-arctic atmospheres. Although slightly low, these values compare reasonably to the value of 1quotedbyClerbauxet al. [23] and those found by Ravazi et al., who found DFS of 1.16, 1.04 and 0.92 for their considered scenes (tropical, mid-latitude and polar regions respectively), also finding the polar regions to be have the least information. Having approximately 1 degree of freedom suggests that a column amount can be calculated but an accurate profile retrieval will be difficult. In fact it is suggested that it may not even be possible to calculate partial column retrievals [22]. 21

23 It should also be noted that little information appears to be coming from the tropical atmosphere, 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 only random errors, this reduction in comparison to the other atmospheres does not occur. It is suspected that this arises from the error due to atmospheric temperature and should be investigated further. Although the result from this study is lower than published figures, it must be noted that DFS depend upon the initial assumptions of the study. If the systematic errors were not considered, the resulting DFS would be greater O 3 The ozone channel selection was carried out in the same way as that for methane, again stopping the selection once the increase in DFS was less than 0.5. The selected 13 channels lie in the strong ozone band around 9.6 µm. This region, cm 1, also used by Boynard et al. [24], minimises the influence of interfering gases (especially water vapour). The positions of the channels selected can be seen in figure delta T, K Brightness Temperature, K O3 CO2 CO H2O N2O CH Wavenumber, cm -1 Figure 4.10: The 13 O 3 channels selected From the 13 selected channels, DOFS are available; 1.77 per profile. Again the polar regions contribute the least information, but this is expected as it has been shown that information increases with increasing surface temperature [25]. This value is slightly lower than many published figures; Clerbaux et al. claim to be able to obtain vertical resolution (DOFS) of 3 4asdoEremenkoet al. [18] (although it is stated that the latter method is mainly for unusual O 3 concentrations). However, Keim et al. [26], who compare several ozone inversion algorithms, show varying numbers of DFS, from , using the LATMOS (Laboratoire Atmospheres, Milieux, Observations Spatiales, France) inversion scheme, up to 3.7 for the LPMAA (Laboratoire de Physique Moleculaire pour l Atmosphere et l Astrophysique, France) inversion scheme. Again although the result from this study is on the low end of this variation, this could be in part due to the initial assumptions and systematic error inclusion. 22

24 Chapter 5 Channel Selection for Trace Gases 5.1 Impact of Stratospheric knowledge on Tropospheric Retrievals This chapter studies the impact of using nadir instruments, such as IASI, alongside instruments with different viewing geometries, and whether this can improve retrieval accuracy. The main focus is on tropospheric concentration retrievals Limb-viewing Instruments The Michelson Interferometor for Passive Atmospheric Sounding, MIPAS The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was launched on March 1st 2002 as one of the core instruments on board ESA s ENVISAT satellite. It is a Fourier transform spectrometer used to detect atmospheric limb emission spectra in the middle and upper atmosphere (the upper troposphere, stratosphere and mesosphere) enhancing studies on atmospheric composition, dynamics and radiation balance [27]. MIPAS observes a wide spectral range within the mid-infrared, operating from 685 cm cm 1 and observes the atmosphere along tangential optical paths with an altitude range of 6 68 km. The viewing geometry can be seen in figure 5.1 and global coverage is available within 3 days. Information gained from MIPAS can be used 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

25 IASI, however the observable altitude is limited to heights above clouds, and hence, tropospheric knowledge is limited [28] Atmospheric Sounding of Methane, CH 4 Methane is a very important anthropogenic greenhouse gas, second only to Carbon Dioxide, CO 2, [29]. Hence, knowledge of its emission sources and sinks is essential in the prediction of future concentrations. Despite this, local emissions, which can be separated into natural sources (wetlands, forests, fires and termites) and anthropogenic sources (rice paddies, livestock, landfill, biomass burning and fossil fuels), are still poorly quantified [22]. Many space borne instruments give information on the methane distribution, however they are usually only sensitive to certain levels in the atmosphere. For example, limb viewing instruments such as MIPAS (the Michelson Interferometer for Passive Atmospheric Sounding), ACE (the Atmospheric Chemistry Experiment) and HALOE (the Halogen Occultation Experiment) provide good vertical resolution but only in the upper troposphere and stratosphere. Nadir viewing infrared instruments, including IASI, AIRS (the Atmospheric Infrared Sounder) and SCIAMACHY have given global concentrations but 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 is closer 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 emission sources; a region that instruments are not sensitive to. This study looks at how using different viewing instruments together may improve our knowledge of this region. Essentially, combining the information retrieved by IASI in the UTLS with that of MIPAS, which gives an accurate stratospheric column amount, should result in an improvement in the accuracy of tropospheric column 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 information in the upper troposphere and lower stratosphere. Although neither instrument is able to sound the lower troposphere well, the combination of the two sets of measurements has the potential to improve retrievals in this region. 24