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LMDObservatoire de Bordeaux

Ref: MAMBO-LMD-TN-043Issue: 1 - Version C

date November 14, 2002

TECHNICAL NOTE

Reference: MAMBO-LMD-TN-043Issue: 1Version: CConfidentiality level: 1 (not restricted)

Sender: IPSL/LMD K. DASSAS

Diffusion to: IPSL/LMD F. FORGETOP/LERMA G. BEAUDIN, B. THOMAS, F. GADEA,

M. GHEUDIN, B. GERMAIN, A. MAESTRINIOB/L3AB P. RICAUD, J. URBAN

Copy to: MAMBO Team

Subject: MAMBO - Data binning - Issue 1 - rev. C

Prepared by: IPSL/LMD K. DASSAS date: October 18,2002

Approved by: OB/L3AB J. URBAN date: October 30,2002

Validated by: IPSL/LMD F. FORGET date: November 14,2002

1



date November 14, 2002i

Contents

1 Introduction 1

2 Method description 2

3 Study on H � O 53.1 Curvature calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2 Choice of a frequency grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3 Tests and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.3.1 Tests for altitude below 65km . . . . . . . . . . . . . . . . . . . . . . . 73.3.2 Tests for altitude above 65km . . . . . . . . . . . . . . . . . . . . . . . 9

3.4 Conclusion for H � O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.5 Mapping table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4 Apply on other target molecules 124.1 Data binning for CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.1.1 3D map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.1.2 Tests and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.1.3 Conclusion for CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.2 Data binning for��

CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.2.1 3D map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.2.2 Tests and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.2.3 Conclusion for

��

CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Data binning for molecules without CTS 185.1 Tests and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185.2 Conclusion for HDO and H � O � . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

6 Summary and Conclusions 19

A Molecules profiles 20

B Validations tests 21

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date November 14, 2002ii

List of Figures

1 spectrometer architecture baseline . . . . . . . . . . . . . . . . . . . . . . . . . 12 method description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 H � O retrieval error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 H � Ospectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 325 grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 tests H � O 0-65km . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 tests H � O 65-100km . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 345 grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 tests CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1310 330 grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1511 tests

��

CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1612 tests HDO - H � O � . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1813 validation tests CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

List of Tables

1 Data binning for H2O: conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 102 mapping table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Data binning for CO: conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Data binning for

��

CO: conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 175 Data binning: general conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 19

ii




1 Introduction

As described in the MAMBO CNES proposal, 73 set of spectra (2*35 at the limb and 3 at nadir)are acquired by MAMBO each 120s. The complete acquisition sequence data rate for MAMBO is16Mbits over one cycle of 105s integration time (120-sec cycle total duration) : each acquisitioncontains 1024 ACS (8x128) channels + 4000 CTS channels, coded on 16bits. We present belowthe final spectrometer design as defined in the CNES proposal and modified in MAMBO-LMD-TN-042 available on the mambo website. The total data rate for a complete acquisition sequencewith this spectrometer design is 40 Kbits/s or 3.4 Gb/day.

Figure 1: Spectrometer architecture baseline

In practice, a major part of the spectrometer channels does not contain independant infor-mation on the altitude profile of the emitting species.. The objective of this report is therefore toexploit a possibility to reduce and control the data rate; we will sort, bin, and compress the spectraldata on the following basis:

� The highest spectral resolution is not necessary on the entire CTS bandwidth, but only inthe line center. It can be progressively degraded 5 to 10 MHz from the centre.

� Many spectral lines become narrow above 40km, and except for CO, most disappear above70km.

The philosophy of our approach is to determine different grids (frequency x altitudes) withadapted spectral resolution. In the first part of this study, we will present a method based on theuse of the spectrum curvature. This method intends to find a correspondance between curvatureand spectral resolution which will be described in a table. This table will be called the mappingtable in this report. In the second part, we apply this method to H � O in order to define the mappingtable. In the third part, we apply the mapping table to CO and

��

CO, the two other target moleculesassociated with a high resolution spectrometer.

1




2 Method description

We first want to explain the choice of using the curvature d�

Tb/(df)�

in order to bin the data.The spectral shape of an emission line to be observed by MAMBO is determined by the geometryof the radiative transfer, the spectroscopic properties of the transition, and the abundance of theemitting gas. Important spectroscopic properties of the transition are the line strength as well asline broadening processes such as pressure (collisional) and temperature (Doppler) broadening,leading to Lorentzian or Gaussian line shapes, respectively. The line shape, observed duringa measurement and usually well modeled using a Voigt-function taking into account the twodifferent effects, thus contains important information on the abundance of the observed gas. Inorder to make full use of this information contained in a measurement the spectral resolution ofthe observing system should be well adapted to resolve the lineshape. A high resolution is thusrequired were the curvature (second derivative) of the spectrum is high, while a low resolutionis permitted in spectral regions where the change of the observed brightness temperature withfrequency is expected to be linear.

The different steps of the approach are described in detail below.

325.1000 325.1200 325.1400 325.1600 325.1800 325.2000frequency [GHz]

0

20

40

60

80

altit

ude

[km

]

curvature H2O max - CTS

1.0•102

5.0•102

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107

Figure 2: block diagram describing approach for channel binning

Step 1: Using the forward model Moliere-5 (P. Baron, J.Urban - Bordeaux Observatory),calculations of brightness temperatures for the band around the target molecule (ACS bandwidth)are performed.

2




Step 2: Calculations of the absolute value of curvature d�

Tb/(df)�

for each altitude. Thisallows to create a 3D map (contours of the curvature versus frequency and altitude). Three kindsof maps are generated corresponding to three different scales. One for the total bandwidth (2GHzfor H � O), one for the CTS bandwidth (120MHz for H � O), and one bigger zoom (10MHz on theleft of the line centre).

Step 3: Using the 3 maps generated during step 2, different grids (frequency - altitude) withdifferent spectral resolution can be defined. This is an empirical approach. We can see thatin the centre of the line, and especially at high altitudes, the curvature reachs its maximumvalue. For big values of the curvature, we want to choose high spectral resolution. For lowervalues, we’ll choose lower spectral resolution. The main issue here is to define the right grid limits.

Step 4: Using Moliere-5, we proceed to forward and inversion calculations using the new fre-quency grid. All other parameters are the same as in the simulation of reference (1s of integrationtime every 4km, 23cm antenna, Tsys=3178K). The linear retrieval algorithm employed for theinverse problem is based on the Optimal Estimation Method (OEM). This linear least-squaresmethod combines statistical a priori knowledge on the variability of the searched parameters withthe information provided by the measurement, using the associated errors as weights. The totalerror is the sum of different contributions: the error due to statistical measurement noise andthe smoothing error due to limited altitude resolution. Figure 3 shows an example of retrievalsimulation results.

Figure 3: H � O retrieval simulation using Moliere-5

Step 5: In order to check that retrieval information obtained with a simulation using the newfrequency grid is not degraded compared to retrieval information obtained with a simulationusing the original frequency grid, we calculate the following ratio: (total retrieval error new - totalretrieval error reference)/(total retrieval error reference) * 100.

3




If this ratio is greater than 0.5%, we decide that the new frequency grid is not well adapted, whatmeans that the spectral resolution is too degraded. We then have to change the grid. That alsomeans that we have to change the mapping between curvature and spectral resolution. A goodcompromise has to be found between the number of channels and the quality of the retrievalresults.If the error ratio is lower than 0.5%, we decide to accept the new grid.

Step 6: Once we have defined a correct frequency grid, we are able to define the final mappingtable which will be used to create new grids for the other target molecules.

Step 7: We apply the mapping table to CO and��

CO. We can also perform some other testssimilar to step 5 in order to validate the mapping table on other molecules.

4




3 Study on H � O

In order to find a mapping table which could define a correspondance between spectral resolutionand curvature, we decided to study H � O with a typical VMR profile for a wet atmosphere.

3.1 Curvature calculation

We present below the results of step 1 and 2 as described in the first part.

Figure 4: H � O spectra, slopes and curvatures (absolute values)

We can see that we have high curvature values for high altitudes and for frequencies aroundthe line centre.

5




3.2 Choice of a frequency grid

Figure 5 represents two 3D maps (contours of the curvature versus frequency and altitude) ; re-spectively one for the CTS bandwidth and one for a 10MHz bandwidth. We have applied a fre-quency grid which is the initial guess for this data binning. The goal is to have a high spectralresolution for high values of the curvature.

400KHz

400KHz

1000KHz

4MHz1MHz1MHz4MHz

1000KHz

200KHz 100KHz

5MHz/2

20MH/2

10MHz/2

1000KHz

Figure 5: H � O - initial frequency grid - left (fig 5a): CTS bandwidth (120MHz)- right (fig 5b ):zoom 10MHz bandwidth. Each box defines the ranges in altitude and frequency where a certainspectral resolution has been chosen to bin the data.

Looking at figure 5, we can for instance guess that for curvature values greater than 10 � , weneed a very high spectral resolution (100KHz). For values greater than 10

�, we can choose a

spectral resolution of 200KHz. For values greater than 10�, we can choose a spectral resolution of

400KHz. For values greater than 10�, we can try a spectral resolution of 1MHz. A first guess could

then be: For altitudes up to 65km, we use 100KHz of spectral resolution on a bandwidth of 5MHzaround the line centre, then 200KHz on 10MHz, 400KHz on 30MHz, 1MHz on 120MHz, and8MHz on 2048MHz. For altitudes from 65km to 100km, we use 100KHz of spectral resolutionon a bandwidth of 5MHz, 400KHz on 10MHz, 1MHz on 30MHz and 4MHz on 120MHz, and40MHz on 2048MHz. For altitudes from 100km to 130km, we use 100KHz of spectral resolutionon a 4MHz bandwidth (there is almost no more H � O above 70km). We can even decide not toscan these altitudes for H � O.

6




3.3 Tests and results

Two sets of tests have been performed. One set of simulations for the first altitude window (0-65km) and a second set for another altitude window (65-100km). The limit of 65km has beenempirically chosen according to curvature values shown in figure 5.

3.3.1 Tests for altitude below 65km

The following tests correspond to step 4 as described before. Results correspond to step 5.

Figure 6: H � O - VMR retrieval - impact of data binning: study from 0 to 65km - Upper left: fig 6a(bandwidth limit for using 100KHz as spectral resolution) - Upper right: fig 6b (200 KHz limit) -Lower left: fig 6c (400KHz limit) - Lower right: fig 6d (1000KHz limit)

Looking at the zoomed 3D map (figure 5b), we can be tempted to set 10 � as a limit for100KHz of spectral resolution, what corresponds to a bandwidth of 5MHz around the line centre.Using 5MHz with 100KHz of spectral resolution indeed yields good results, however if weperform other tests in order to refine the minimum bandwidth needed with 100KHz, we seethat 3MHz is enough, but that 1MHz drastically increases the error around 80km, as shown in

7




figure 6a. Looking at the 3D map, we can decide that the 100KHz limit matches with a curvaturevalue of 10

�

(blue curve). In order to have a reasonable margin, we decide to keep 10 � as limit fora spectral resolution of 100KHz.

Figure 6b clearly shows that using a bandwidth of 5MHz with 100KHz of spectral resolutionyields as good results as using a 10MHz bandwidth with 100KHz of spectral resolution but only ifwe use a spectral resolution of 200KHz in the 10MHz around this 5MHz bandwidth. If we use aspectral resolution of 400KHZ instead of 200KHz in that zone, the error increases around 60km.Looking a the zoomed 3D map we can see that 10MHz bandwidth corresponds to a curvature of10

�around 60km. We can thus decide to use 200KHz of spectral resolution for curvature values

greater than 10�

and lower than 10 � . We could of course get a more accurate limit for 200KHzbut this limit is acceptable for the scope of our study (the gain in data reduction would not bemuch greater).

Figure 6c shows results of the study of a limit for the use of 400KHz as spectral resolution. Ifwe use a 20MHz bandwidth with 400KHz as spectral resolution instead of 30MHz with 400KHz,the error does not increase significantly. If we use a 110MHz bandwidth with 400KHz as spectralresolution instead of 30MHz with 400KHz, we only have a very slight difference between bothconfigurations. If we use a 10MHz bandwidth with 100KHz and then 120MHz with 1000KHz,without passing through the stage of using an intermediate spectral resolution of 400KHz, resultsare not significantly degraded; however, we do not want to ignore the difference in error retrievalaround 50km for this last configuration. In order to keep a margin of security, 30MHz bandwidthwith 400KHz of spectral resolution seems to be a suitable solution. We can conclude that we arenot able to define here a clear limit for the use of 400KHz as spectral resolution. We can guessthat this limit is close to 15MHz (corresponding curvature values are between 5x10

�and 10

�

according to figure 5.

Let’s now investigate for the 1000KHz limit. Figure 6d allows us to propose a mappingbetween curvature and spectral resolution for the 1000KHz limit. If we use 10MHz with 100KHz,30MHz with 400KHz and 120MHz with 1000KHz as spectral resolution, results are not reallyimproved compared to the same configuration with 50MHz with 1000KHz of spectral resolutioninstead of 120MHz. But if we choose 30-120MHz with 4000KHz of spectral resolution insteadof 30-50 with 1000KHz and 50-120MHz with 4000KHz, the error increases more significantlyaround 45 km. Looking at the 3D map, we see that a bandwidth of 50MHz around the linecentre corresponds to an approximative value of 5x10

�for the curvature around 45km. We could

propose to use a spectral resolution of 1000KHz when the curvature is greater than 5x10�. But

we have just defined here a lower limit for the use of 1000KHz as spectral resolution. Whenthe curvature increases, we have to use a higher spectral resolution. With the test described infigure 6c, we chose to have a spectral resolution of 400KHz for a bandwidth of 10-30MHz aroundthe line centre, and 100KHz on 10MHz around the line centre.

8




To summarize, the new mapping table used to bin the spectral data could be:

� 100KHz for curvature greater than 10 � K/(GHz)�

� 200KHz for curvature lower than 10 � K/(GHz)�

and greater than 10�

K/(GHz)�

� 400KHz for curvature lower than 10�

K/(GHz)�

and greater than 5x10�

K/(GHz)�

� 1000KHz for curvature lower than 5x10�

K/(GHz)�


K/(GHz)�

� 4000KHz for curvature lower than 10�

K/(GHz)�


K/(GHz)�

3.3.2 Tests for altitude above 65km

In order to confirm this mapping table, and to obtain the new grid frequency for the second windowaltitude, we are now presenting tests and results for altitudes from 65km to 100km.

Figure 7: H � O - VMR retrieval - impact of data binning: study from 65km to 100km - left: fig 7a -right: fig 7b

Looking at the zoomed 3D map, we see that above 65km, we have to choose a 4MHz band-width to surround curvature value greater than 10 � (100KHz limit) , a 5MHz bandwidth to sur-round curvature values greater than 10

�(200KHz limit), and 6MHz bandwidth to surround curva-

ture values greater than 5x10�

(400KHz limit). In order to simplify, we can use 6MHz bandwidthwith 100KHz and then directly use 20MHz bandwidth with 1000KHz as spectral resolution.

Figure 7a also shows that using 60MHz as spectral resolution above 60MHz from the linecentre is acceptable compared of the use of 8MHz as spectral resolution for the same bandwidth.We have to keep in mind that we are talking here of the second altitude window (65 to 100km).

Figure 7b show results which validate the mapping table by confirming the limit for 1000KHz.This is just an example of several validation tests we have performed. According to the predefinedmapping table, we should take a curvature value of 10

�as a limit for 1000KHz of spectral reso-

lution. Looking at the 3D map, we have to choose a 20MHz bandwidth around the line centre to

9




surround a curvature value of 10�. Figure 7b shows that using 20MHz - 1000KHz indeed yields

as good results as using 30MHz with 1000KHz of spectral resolution whereas using 4000KHz forspectral resolution from 10 to 120MHz degrades the results.

3.4 Conclusion for H � O

From the tests above, we can conclude that a correct data binning for H � O could be defined asfollow:

Data binning for H � Orange of altitudes: 0 - 65km (acquisition of 19 spectra )distance from the line center spectral resolution total number of channels0-5MHz 100KHz5-15MHz 400KHz15-60MHz 1000KHz60-1024MHz 8000KHz 480 channels

range of altitudes: 65 - 105km (acquisition of 10 spectra)distance from the line center spectral resolution total number of channels0-3MHz 100KHz3-10MHz 1000KHz10-60MHz 4000KHz60-1024MHz 60000KHz 130 channels

range of altitudes: 105 - 130km (acquisition of 7 spectra)distance from the line center spectral resolution total number of channels0-2MHz 100KHz 40 channels

Table 1: Data binning for H2O: conclusion

ch: channel - s: spectra acquisition480ch x 19s + 130ch x 10s + 40ch x 7s = 10700 spectral points are acquired every limb scaninstead of 51876 (36 spectra acquisitions x 1441 channels) initially. For H � O, the compressionratio is 4.84. We want to highlight the fact that this is not a descope solution. We could easilyincrease the compression ratio if neededwithout much additional loss.

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3.5 Mapping table

As described in step 6, we can now propose a universal mapping table:

curvature K/(GHz)�

spectral resolution� 10 � 100KHz10

�- 10 � 200KHz

5x10�

- 10�

400KHz10

�- 5x10

�1000KHz

10�

- 10�

4000KHz

Table 2: mapping table

This mapping table works for our purpose. But in order to define an accurate mapping table,we should perform many other tests. We decided for instance to choose 10

�as a limit for 200KHz

of spectral resolution. But we do not know for instance if the real limit is either 10�

or 2x10�.

Anyway, validation tests have been performed with the CO line (tests and results in Annexe B),which confirm the suitability of this mapping table.

11




4 Apply on other target molecules

We now want to apply the mapping table to other target molecules associated with a CTS. CO and��

CO are respectively associated with a 200MHz-100KHz CTS and with a 80MHz-100KHz CTS.

4.1 Data binning for CO

4.1.1 3D map

Figure 8 shows the new frequency grid we want to use for CO retrievals according to the predefinedmapping table.As for H � O, two different altitude windows have been defined. Looking at the 3D maps, wedecided to create a first altitude window from 0 to 80km, and a second one from 80 to 130km.

345.7880 345.7900 345.7920 345.7940frequency [GHz]

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tude [km

]zoom curvature 12CO 20MHz/2

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100KHz

4MHz 4MHz

4MHz 1MHz 1MHz 4MHz

4MHz 1MHz 100KHz

400KHz

Figure 8: CO - initial frequency grid - left (a): CTS bandwidth (200MHz)- right (b): zoom 10MHzbandwidth

According to the mapping table, we have defined the following grids:

� from 0 to 80km, we chose a 10MHz bandwidth with 100KHz of spectral resolution, then40MHz with 400KHz, 100MHz with 1000KHz, 200MHz with 200KHz and 2048MHz with14MHz.

� from 80 to 130km, 4MHz bandwidth with 100KHz of spectral resolution, 10MHz with1000KHz, 80MHz with 4000KHz and 2048MHz with 70MHz.

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4.1.2 Tests and results

We have to keep in mind that MAMBO will retrieve temperature profiles using CO (and��

CO)This is the reason why tests have been performed for VMR and T retrieval. We present here(fig 9a and fig 9b) results with VMR retrieval, and (fig 9c and fig 9d) results with T retrieval. Tand CO have been retrieved separately for the purpose of this case study.

Figure 9: impact of data binning - CO - mixing ratio retrieval - upper left: fig 9a (0-80km) - upperright: fig 9b (80-130km) - Temperature retrieval using CO: lower left: fig 9c (0-80km) - lowerright: fig 9d (80-130km)

As expected, the ratio between the error obtained with simulations using the original spec-trometer configuration and the error with the new frequency grid is lower than 0.5 %.

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4.1.3 Conclusion for CO

From the tests above, we can conclude that a correct data binning for CO could be defined asfollow:

Data binning for COrange of altitudes: 0 - 80km (acquisition of 23 spectra )distance from the line center spectral resolution total number of channels0-5MHz 100KHz5-20MHz 400KHz20-50MHz 1000KHz50-1024MHz 14000KHz 338 channels


Table 3: Data binning for CO: conclusion

ch: channel - s: spectra acquisition338ch x 23s + 190ch x 13s = 8944 spectral points every limb scan instead of 2232 channels x36 spectra acquisitions = 80352 spectral points with the original frequency grid. For CO, thecompression ratio after data binning is approximatively 9.

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4.2 Data binning for��

CO

4.2.1 3D map

Figure 10 shows the new frequency grid we want to use for CO according to the predefinedmapping table.

330.5800 330.5820 330.5840 330.5860 330.5880frequency [GHz]

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tud

e [

km]

zoom curvature 13CO 20MHz/2

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330.5600 330.5800 330.6000 330.6200frequency [GHz]

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ude

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]

curvature 13CO - CTS

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5.0•104

5.0•

104

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1.0•

105

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4MHz

1MHz 1MHz

100KHz

400KHz

100KHz

4MHz 1MHz 100KHz

4MHz

Figure 10:��

CO - initial frequency grid - left: fig 10a : CTS bandwidth (80MHz) - right fig 10b:zoom 10MHz bandwidth

As for H � O and for CO, different altitude windows have been defined. Looking at the 3Dmaps, we decided to create a first altitude window from 0 to 60km, a second one from 60 to100km and a third one from 100 to 130km.

According to the mapping table, we have defined the following grids:

� from 0 to 60km, we chose a 10MHz bandwidth with 100KHz of spectral resolution, 20MHzwith 400KHz, 80MHz with 1000KHz, and 512MHz with 70MHz.

� from 60 to 100km, 4MHz bandwidth with 100KHz of spectral resolution, 8MHz with1000KHz, 80MHz with 4000KHz and 512MHz with 70MHz.

� from 100 to 130km, 3MHz bandwidth with 100KHz of spectral resolution, 2048MHz with250MHz.

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4.2.2 Tests and results

As for the CO line, the��

CO line is used to retrieve temperature. This is the reason why testshave been performed for VMR and T retrieval. We present here (figure 11a-b) results with VMRretrieval, and (figure 11c-d) results with T retrieval. T and

��

CO have not been simultenouslyretrieved.

Figure 11: impact of data binning -��

CO - mixing ratio retrieval - upper left: fig 11a (0-60km) -upper right: fig 11b (60-100km) - Temperature retrieval using

��

CO: lower left: fig 11c (0-60km)- lower right: fig 11d (60-100km)

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4.2.3 Conclusion for��

CO

From the previous tests, we can conclude that a correct data binning for��

CO could be defined asfollow:

Data binning for��

COrange of altitudes: 0 - 60km (acquisition of 18 spectra )distance from the line center spectral resolution total number of channels0-5MHz 100KHz5-10MHz 400KHz10-40MHz 1000KHz40-256MHz 8000KHz 292 channels


range of altitudes: 100 - 130km (acquisition of 8 spectra)distance from the line center spectral resolution total number of channels0-1.5MHz 100KHz 30 channels

Table 4: Data binning for��

CO: conclusion

ch: channel - s: spectra acquisition292ch x 18s + 68ch x 10s + 30ch x 8s = 6176 spectral points every limb scan instead of 908 x 36= 32688 spectra points with the original frequency grid. For

��

CO, the compression ratio afterdata binning is approximatively 5.2.

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5 Data binning for molecules without CTS

The former study enabled to reduce the data flow for molecules associated with a high resolutionCTS. For molecules associated with ACS, the initial number of channels is much lower than formolecules associated with CTS: 128 channels for each molecule (O � , H � O � , HDO).

5.1 Tests and results

We do not want here to reduce the data flow by reducing the number of channels because wealready have an optimized spectral resolution. However, we can reduce the data flow by reducingthe window altitude for HDO and H � O � , as their mixing ratio become very low above 60km forHDO and above 50km for H � O � . We are talking here about high HDO and H � O � concentrationprofiles. Once again, we want to keep a margin. Instead of removing all data above those altitudelimits, we want to collect data up to 130km. According to H � O � and HDO 3D maps, we decideto keep a spectral resolution of 2MHz in a bandwidth of 20MHz. Figure 12 presents results ofthe error comparison for these molecules with a new frequency grid. This new grid is designed asfollows: 20MHz in the line centre with 2MHz of spectral resolution, and then only 2 channels forthe rest of the initial bandwidth (256MHz).

For O � (high concentration profile), we still detect the molecule up to 80km-90km and no databinning will be done on O � .

Figure 12: - VMR retrieval - impact of data binning.left: HDO (60 to 100km) - right: H � O � (50 to 100km)

The goal of staying under the threshold of 0.5% is reached for the target altitudes.

5.2 Conclusion for HDO and H � O �

For HDO and H � O � , we had 128 channels x 36 spectrum acquisitions = 4608 spectral points foreach with the original configuration. After data binning, we have for HDO: 128 channels x 18spectrum acquisitions + 12 channels x 18 spectrum acquisitions = 2520 spectra points. For H � O � :

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128 channels x 16 spectra acquisitions + 12 channels x 20 spectra acquisitions = 2288 spectralpoints.

6 Summary and Conclusions

Two major points can be retained from this study:

� A method based on the mapping between curvature and spectral resolution has been testedand validated in the scope of our study. The mapping table which has been defined hereshould be useful for other data binning. Validation tests have been performed on CO (testsand results can be found in Annexe B) to check the mapping table defined after the study onH � O. However further work should be done to refine this mapping.

� This methods allows us to reduce the data rate for MAMBO by a factor of 5.

molecules initial number of number of spectral points compressing ratiospectral points after data binning

H � O 51876 10700 4.8CO 86352 8944 8.9��

CO 32688 6192 5.2HDO 4608 2520 1.8H � O � 4608 2288 2.0O � 4608 4608 1.0

total 184740 35318 5.2

Table 5: Data binning: general conclusion

The total data rate without data binning was 3.4 Gb/day. After data binning we can reach 680Mbits/day. This rate worth for a 16 bits coding mode. If we take into account the noise level, thespectral data can be coded on only 12 bits, what reduce the data flow by 1.3 (523 Mbits/day). Thisshould be the baseline data rate for MAMBO.However, during mission phases when Mars Earth data rate is low or when MAMBO is not apriority, further data binning could be performed. The mapping table we defined could be refinedmore accurately and more strictly applied. In our study, we often chose to use larger limits than theone we could use (for instance, 10MHz with 100KHz instead of 5MHz with 100KHz and 10MHzwith 200KHz). We could also decrease the number of channels by accepting 1% of difference inthe error ratio instead of 0.5%.Another study could be conducted to define a descope data rate (100 Mbits/day ?). Note that, asdescribed in the MAMBO proposal, complete sets of full spectral data (without any compression)should be sent to Earth on a regular basis to account for any ”surprised” line or an unexpectedinstrument effect.

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A Molecules profiles

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B Validations tests

Figure 13: CO - VMR retrieval - error comparison Upper left: fig 14a (100KHz limit) - Upperright: fig 14b (200 KHz limit) - Lower left: Fig 14c (400KHz limit) - Lower right: Fig 14d(1000KHz limit)

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lmd issue: 1 - version c observatoire de bordeaux date … · 2003-02-05 · lmd observatoire de...

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