The CryoSat-2 level 0 to 1b science processors were developed and implemented during the phase C/D of the CryoSat-1 development. The baseline for CryoSat-2 will be to maintain these processors as is and upgrade in due course to take into account expected minor changes to the CryoSat-2 hardware.

In this paper we present the methods used to process the three modes of science operation. These are 1. pulse-width limited low resolution mode (LRM), 2. pulse-width limited and phase coherent single channel synthetic aperture radar mode (SARM) and 3. the dual channel pulse-width and phase coherent synthetic aperture radar interferometric mode (SARInM). We also cover how internal calibration corrections are applied within the processor but leave the detail of how those corrections are generated to a separate paper.

We discuss key elements regarding the challenges of commissioning CryoSat level 0 to Level 1b processors and provide evidence of algorithm verification using simulations and data acquired over Arctic land/sea ice using ESA’s airborne advanced synthetic interferometric radar altimeter system (ASIRAS). ESA’s ASIRAS processor, housed and operational at the German Alfred Wegener Institute (AWI), has provided processed data sets for 3 extensive Arctic land and sea ice campaigns (CryoSat validation experiments – CryoVEx) and numerous test campaigns over the past few years. Since the ASIRAS level 1b processing algorithms, though tailored for use with a low altitude airborne system, are more or less identical to those of the CryoSat instrument processing facility we show pre-launch initial verification of the CryoSat level 1b SAR/SARIn science processor design.

Finally, since CryoSat-2 will be operated over already identified regions of ocean using SAR and SARIn mode for purposes of external calibration and for the planned provision of data as part of the original CryoSat-1 data Announcements of Opportunity (AO) we discuss the potential for using the existing SAR processor algorithms for future ESA operational missions such as Sentinel-3 SRAL ocean SAR mode. We further discuss the appropriate use of existing algorithms for SRAL for land SAR mode.

CryoSat was the first of the ESA Earth Explorer opportunity missions, a mission proposed by the science community in 1998 by Wingham et al. The proposal was underpinned by an urgency to reduce uncertainties in elevation measurements over land and sea ice fields not available using pulse-width limited radar altimetry. CryoSat was to be launched and placed in an orbital inclination of 92º on 8th October 2005; however, the Eurokot launcher failed a few minutes into the launch sequence resulting in the loss of the mission. Since the improvement in the understanding of key climate issues that instigated the original CryoSat proposal are now more urgent than ever that on Feb 24th 2006 the ESA Programme Board for Earth Observation approved a recovery mission and ESA are procuring CryoSat-2 for a launch in 2009.

Taking into account the original CryoSat mission and science requirements, (Wingham, 1999) are unchanged, this paper describes the current state of CryoSat Instrument Processing Facility level 1b processor algorithm verification by means of simulation results, independent airborne ASIRAS and in-situ measurements obtained from the CryoSat Validation Experiments (CryoVEx) in the Arctic between 2004 and 2007 (Cullen et al, 2007) and plans for further verification. We briefly discuss the use of the existing algorithms as a baseline for the synthetic radar altimeter (SRAL), the GMES Sentinel-3 mission topography payload similar in many respects to SIRAL.

2.

2.1. The System A full description of the system and orbit characteristics is provided in (Francis, 2007). CryoSat-2 will be yaw steered local Earth pointing platform placed into a drifting orbit of inclination ~92° with an eccentricity of ~0.001406 and mean orbital altitude of ~717 Km. Its main payload is a 13.6 GHz full de-ramp nadir pointing synthetic aperture interferometric radar altimeter (SIRAL) which generates phase coherent pulse-width limited echoes allowing azimuth processing to improve along track sampling of a surface (SARM mode). The two receiving antenna of SIRAL-2 (transmission

ESA’S CRYOSAT-2 MULTI-MODE LEVEL 0 TO LEVEL 1B SCIENCE PROCESSORS – ALGORITHM DESIGN AND PRE-LAUNCH VERIFICATION WITH ASIRAS

R. Cullen (1) D.J. Wingham(2), P. Viau (1), C.R. Francis(1) & C. Mavrocordatos(1)

(1)European Space Agency, ESTEC, Keplerlaan 1, Postbus 299, 2200 AG Noordwijk, The Netherlands. (2)Department of Earth Sciences, University College London, Gower Street, London WC1E 6BT, UK.

ABSTRACT 1. INTRODUCTION

THE CRYOSAT-2 SYSTEM, SIRAL-2 AND THE GROUND SEGMENT PRODUCTS

_____________________________________________________

Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

through one) are aligned in the across track direction such that in the ground processing the across-track echoing point can be determined over ice sheet margins via the use of interferometric phase processing (provided the across track slope is not too high and interferometric phase is not wrapped). The instrument also has a low resolution mode (LRM) similar to the functionality of the EnviSat RA-2, for example, for usage over land ice interior and ocean/land calibration announcement of opportunity (AO) zones (see Figure 2-2). SIRAL-2 is designed as a replica of SIRAL with the addition of some improvements. The main improvement concerns the SIRAL-2 and the addition of a redundant instrument (see Figure 2-1) in order to improve overall system reliability.

Figure 2-1 SIRAL-2 redundant hardware configuration.

2.2. Ground Segment Products The following table provides a list of both science only data products (described further in the Mission and Data Description Document, Francis, 2007 and Wingham, 2006) from level 0 to level 2. One can see that the earliest stage data becomes of use is at level 1b following correction of internal instrument distortions and geo-located multi-looked echoes (SAR/SARIn modes).

Product Type

User Distribution

Mode Description

LRM The on-board average of 91 range (frequency domain) pulse-width limited power waveforms at ~ 20Hz

SARM Bursts of single chain 64 individual time domain pulse-width limited complex waveforms.

L0 No

SARInM Bursts of dual chain 64 individual time domain pulse-width limited complex waveforms.

LRM

SARM

L1A Yes

SARInM

Converted platform units geo-location and timing. The product is not calibrated. Waveforms are a copy of L0.

LRM Calibrated power echoes at ~20Hz

SARM Calibrated multi-looked power echoes at ~20Hz

L1B Yes

SARInM Calibrated multi-looked power, interferometric phase difference and coherence waveforms at ~20 Hz

L2 Yes All (LRM/SARIn) Surface elevation and across track corrected echo location. (SAR) Sea-ice thickness and free-board measurements.

Table 2-1Brief description of product types for all science modes.

Figure 2-2 CryoSat-1 mode masks and AO zones used here to show the science community had a keen interest in using the CryoSat acquisitions over all surface types. Since CryoSat was ruled by strict mass memory and X-band downlink budgets (a single ground station was located in Kiruna) it was unfortunately necessary that planning was undertaken not to record data over most land surfaces. Low rate mode acquisitions over ocean were a pre-requisite to support precise orbit determination. The operational mask will be updated following the 2007 Cal/Val AO outcome.

3. Mode Dependent L0-L1b Processing Algorithms.

Level 0 to level 2 processing is described in Wingham, et al 2006. In terms of implementation, despite differences in level 0 parameters for each of the 3 science modes (Table 2-1) it was decided in the development of the instrument processing facility to make the level 1b consistent between all the modes. The level 0 to level 1A full bit rate (FBR) processor is identical for all 3 modes (see, Figure 3-1). Here instrument source packets are unpacked, de-coded and converted to engineering units. Instrument datation is converted to that representing Earth geometry, orbital state vector and attitude information is interpolated and added to the level 1A product. Waveforms for each mode are simply copied into the product and some calibration values are determined. For LRM, L0 echoes have been range compressed via fast Fourier transform and converted to power on-board SIRAL to give range window echoes as would be normally found in any L0 RA product. For SAR and SARIn modes data are

telemetered in bursts of 64 complex time domain echoes (1 burst per radar cycle of ~46.8 mS for SARIn compared with 4 bursts for SAR in the same period) that have undergone de-ramp (See Chelton, 1989, for an example of the full-de-ramp procedure). The reason for the larger number of bursts for SAR opposed to SARIn is twofold. Primarily this is due to the needs of on-board tracking between the two modes. For SAR, surfaces within the pulse-width limited footprint have an overall low variability of surface elevation hence a 320 MHz bandwidth can be maintained for all 4 bursts in the radar cycle. SARInM requires tracking of ice sheet margins and in this case 3 of the 4 bursts available are measure at 40 MHz. Secondly, sampling of small leads in sea ice is expected to benefit from the larger number of echoes in SAR mode.

Finally, geo-corrections are interpolated from respective data models and added to the L1 product; they are not applied to the data until L2 processing.

PreProcessor_Initialise

Decode_SIRAL_Level_0

Compute_Datation

Compute_Geolocation

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Compute_Gain

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PreProcessor_Initialise

Decode_SIRAL_Level_0

Compute_Datation

Compute_Geolocation

Compute_Sat_&_Baseline

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Figure 3-1 Level 0 to Level 1A (FBR) processor for all modes. The procedure shown is similar to EnviSat RA-2 Level 0 processing.

3.1. Level 1a -1b processing Level 0 to lb processing of pulse-width limited LRM data is very similar to EnviSat RA-2 level 0 to L1b processing and not described further here other than to say following ingestion of L1a, transfer function imperfection and integrated path delay calibration corrections (Cullen et al, 2007) are applied, the effects of Doppler shift on instrument de-ramp are computed and applied to the window delay.

Level 0 to lb processing of pulse-width limited LRM data is very similar to EnviSat RA-2 level 0 to L1b processing and not described further here other than to say following ingestion of L1a, the transfer function imperfection and integrated path delay calibration corrections (Cullen et al, 2007) are applied, the effects of Doppler shift on instrument de-ramp are computed and applied to the window delay.

Figure 3-2 Schematic of the L1A – L1B processing. Difference relating between SIRAL and ASIRAS are due to the nature of the airborne geometry and instrument timing the actual software are independent in terms of implementation.

The solution is to have a fixed instrument PRF with bursts consisting of 64 phase coherent pulses. Any echo from one burst is phase incoherent to echoes from any other burst by choice of the burst repetition interval. The overall complexity of arranging azimuth formed echoes to illuminate the same region on the surface if passed entirely over to the ground segment processing.

The method to generate azimuth formed echoes as shown in Figure 3-3c that illuminate the same region on the surface as opposed to that shown in Figure 3-3a follows. On-board tracking information is available to the ground segment. We use the tracking information to provide an approximation of the surface elevation on the ground track but clearly with some uncertainty. Along the ground track surface locations are determined at intervals determined as a function of satellite and tracker determined surface elevation, instrument wave number and synthetic aperture length (which is a function of speed, instrument PRF and the 64 pulses constituting a burst). Given these locations the 64 azimuth beams (Curlander, 1991) generated from a burst by FFT can be forced to illuminate 64 of the defined locations (Figure 3-3b) via pre FFT phase shifts such that as the satellite approaches and recedes from any given location a series of statistically independent echoes are generated from different bursts (Figure 3-3c). We call this complete collection of complex echoes a stack. At this stage SAR and SARIn mode echoes are

complex time domain though have undergone azimuth processing as described above. All echoes in a stack must be range compressed by FFT and corrected for slant range effects as seen in Figure 3-3d. For this we take a reference range as seen in Figure 3-3d and for each echo a slant range is computed (a correction for Doppler effect on de-ramp is also computed from the velocity vector and angle from satellite per burst to each surface location). Since each range correction will be in total an integer number of and a fraction of range bins we decide to apply the correction in two stages. The fine component is applied as a fine phase correction to each azimuth formed echo, the echo is range compressed using the FFT and then a very simple integer shift is applied. There are some advantages to this method we need to analyse in this paper. Following the process the simple process of summing, multi-looking, all the echoes in a stack and computing the final power echo can take place. For SARIn mode there are two chains of processed data and so there is an extra stage to compute the cross product to extract the interferometric phase difference and coherence waveforms.

For SARIn mode computation of interferometric phase difference is implemented in a straight fo§rward manner from the complex frequency domain stacks (Jensen, 1999 and Wingham et al, 2006).

Products derived from CryoSat are described in Francis et al 2005 and Francis 2007.

(a)

(b)

(c)

(d)

Figure 3-3 Basic SAR/SARIn algorithm in pictorial form.

4. CryoSat Mission Performance Simulator for Verification

The CryoSat Mission Performance Simulator (CRYMPS) was developed in 1999/2000 to provide a test data generation tool in order to allow prototyping of the IPF level 1b and 2 processors for all operational science and calibration modes. End to end scenarios were developed covering point targets, simple surfaces and complex surface types allowing comparison of output level 2 data with known inputs. The results now summarised provide the first stage of verification of the level 1b science algorithms. In most cases test results can be compared by hand calculated values.

4.1. Point Target Simulations The purpose of these many simple tests was to show the simulator and level 1b algorithms were dealing with many issues correctly, namely, orbital and Earth geometry, instrument aspects including phase/amplitude and effects of de-ramp effected Doppler range shifts and across track interferometric phase. With across-track placed point targets it can be shown the interferometric phase difference and inferred angle of arrival are consistent with the placing of the point target and orbital path of the satellite. In a number of cases phase and amplitude distortions were introduced to show the impacts and whether the calibration corrections were being applied correctly.

These simulations were also used as a basis for the development of transponder processing software by a dedicated team described in §6.

Figure 4-1 Interpolated range window power echo containing the response to two point targets; one on the ground track at a surface

location on the earth ellipsoid and the second placed across track by 4km. The exact hand computation of the location of the peak responses

can be determined and checked against results processed through CRYMPS and IPF processors.

Figure 4-2 The interferometric phase difference for the 4km across-track scatter can be extracted at range bin 280.054 and compared

against the hand calculated value.

4.2. Simple Surfaces In order to provide prototype processors with echoes simulated over distributed surfaces and yet designing them such that test results were unambiguous and easy to understand against analytical results it was necessary to build simulations with spherical surfaces and circular orbit. In some cases elevation steps were introduced to show shifting of echoes in range in level 1b products and also provide level 1b echoes that would form the basis of level 2 re-tracking algorithm design for all modes. An example of a typical level 1b echo is provided in Figure 4-3. Although not described further in this paper, many simple simulations specific to different surface types (land ice margin and interior, sea-ice, ocean, lead, etc.) were developed.

Figure 4-3 Level 1b Power echo generated for SARInM for a spherical surface. The discontinuity at range bin ~290 is due to a scatterer integration angle cut-off angle to reduce computation time for echo field data simulation. The ‘toe’ of the echo seen between range bin 110 and 180 is explained in Wingham et al, 2004 and is a result of 1. large azimuth angle beams cutting through many range rings and 2. to a lesser extent azimuth beam side lobe responses incoherent with the reference slant range correction.

4.3. Complex Surfaces – Totten Glacier Example

In order to test robustness of the level 1b algorithms and provide more realistic data sets to allow prototyping and testing of the level 2 processors. This activity could only take place whilst remaining within limits of simulator design, physical surface knowledge and by not introducing complicating factors that could result in an ‘over design’ of the level-2 processors. Many sea-ice cases were developed but here we show one of the land-ice cases. Here we chose the Totten glacier and law dome region of Antarctica. Apart from great interest now shown in this region, at the time of scenario generation the location provided a good example of very low along and across track slopes through to extreme cases that the SIRAL is not actually designed to track on-board. Such an example provided a good basis to test the robustness of the level-2 SARInM re-tracking and echo location method (also LRM for the latter case). An example of the localised DEM and echoes produced is provided in Figure 4-4.

Other surfaces were chosen including the Amery ice shelf and Mac-Robertson land regions of Antarctica. One, however, can only go so far with such simulations before they become so complex they may not actually be representative of how the simplified instrument simulation would represent the real instrument.

Figure 4-4 Totten glacier simulation for SARIn. Top left is the margin of Law Dome in Antarctica whilst the bottom right triangle covers the low slopes of Totten glacier. The design of the simulation was intended to provide a range of minimum to extreme cross-track slopes to test functionality of both level 1 and 2 algorithms. L1b SARIn echoes on the right of the figure show power (black) Interferometric phase difference (red) and phase difference coherence (blue). Wrapping of the interferometric phase difference is evident as expected for slopes of about 0.7º

5. Verification of Algorithms using an Airborne Radar Altimeter (ASIRAS)

The airborne ASIRAS system was developed in order to provide a long term link between the CryoSat retrievals and measurements (for example, see Lenz et al 2002 and Mavrocordatos et al, 2004). The differences between SIRAL and ASIRAS are due to instrument timing, a 320 MHz operating bandwidth opposed to 1 GHz for ASIRAS which is also operated between altitudes of 300 and a few km. In the context of this paper the ASIRAS level 1b processor is very similar to the CryoSat level 1b SAR/SARIn processor, however, there are some subtle differences such that the actual software had to be completely rebuilt for ASIRAS in order to optimise use of memory. ASIRAS also contains some extra functionality (re-tracking since ESA has not developed a level 2 processor and the addition of attitude parameters for which ASIRAS is sensitive and thresholds). We show ASIRAS level 1b results, obtained through previous Arctic campaigns over the past few years, can be compared with data obtained from other instrumentation and in-situ measurements thus verifying the choice for the processing method.

One key validation is to use corner reflectors distributed in the field during campaigns (Francis et al, 2005, for example) for reasons to understand local penetration of integrated radar energy (diversified with sub-surface structure and scattering properties) compared with laser scanner returns known to be from the snow-air interface.

Figure 5-1 T21 region of EGIG line in Greenland in the dry snow zone. A white star indicates the location of a corner reflector.

The data from corner reflectors can be also used for straightforward verification purposes. As an example we take a corner reflector placed at a CryoSat validation site on the Expédition Glaciologique Internationale Groenland (EGIG) line in Greenland as shown in Figure 5-1. By examining the stack obtained over a snow region some tens of meters from the corner reflector, see Figure 5-2, one can see all the slant range aligned range window echoes showing the surface response and what we know to be sub-surface reflecting horizons (described later in this section). However, one also sees the effects of speckle modulated by the ASIRAS antenna pattern and thus as a verification is not of much use. The response from the corner reflector raised ~ 2m above the surface is more clear. Here in Figure 5-3 the response from the corner reflector can be found at the expected surface sample location. Secondly, the response is reasonably clean in power and phase, however, there are some features that are as yet unexplained. In particular, as the aircraft moves away from the sample there appears to be some movement in range that could be due to the 1 Hz under sampling; though this is not proved. It is known by investigation that there is a random datation error that may result be the reason we see some small effects in the stack data and investigations continue.

Figure 5-2 A ‘stack’ of azimuth echoes illuminating a region of about 10 meters across track and ~4 meters

along track. Features at around range bin 120, 170 and 200 are layering under the snow – ice surface.

There are two key published results that show verification of the ASIRAS/CryoSat processing method. The first, Hawley et al, 2006, comes from data acquired from the T21 dry snow zone region of the EGIG line as shown in Figure 5-1. Here the sub-surface layering seen in the stack and level 1b data at this region were compared with data extracted in-situ by the Scott Polar Research Institute (SPRI) using an equipment called a Neutron Scattering Probe that can be used measure the density of snow with depth. Since the speed of light in snow varies with snow density the density profile itself is used to determine the speed of light in the scattering media. A group of some 30 ASIRAS level 1b echoes are averaged to further reduce fluctuations in the signal. Peaks of density are then compared with those shown in the ASIRAS profile and provide a good correlation. It is understood that the neutron scattering probe may not work so well at depths > 6 meters but this is also the case for ASIRAS waveforms which become highly attenuated and hence unreliable after a few metres. However, what this shows is that in principle ASIRAS may be used for long acquisitions over a number of repeats to acquire density profiles without having to disturb the surface

Figure 5-3 A ‘stack’ of azimuth echoes illuminating a region on the ground-track close to the corner reflector. Here, the illuminated azimuth footprints contain the response of a corner reflector at range bin ~60.

More recently Helm et al in 2007 showed that in the percolation zone region of the EGIG line (see Figure 5-5) there is strong evidence of the previous years melt at a distance of about 1.5 meters below the snow-air interface; confirmed by other in-situ measurements. This was achieved by processing a DEM of the region by from data acquired from the airborne laser scanner (ALS) on the same aircraft as ASIRAS, extracting the aircraft ground-track elevation and comparing with ASIRAS waveforms. Here, one can see two distinct peaks in the ASIRAS power waveforms; the first being the air-snow interface and the other being the snow-ice interface from the previous years melt. GPR of the same region also confirmed this.

So there are now a number examples where the ASIRAS processing has been shown to clearly agree with other data sets acquired from independent means. Many more comparisons of this sort will be analysed during the lead up to the CryoSat -2 launch expected in 2009 to further verify functionality. It has been demonstrated aircraft related multi instrument measurements over long distances can be repeated via suitable aircraft planning without disturbing the surface.

Figure 5-4 (Hawley et al., 2006). A Neutron scattering probe measures snow density with depth. Hawley, showed that regions of high density from the probe compare well up to a few metres of depth with layering seen in the ASIRAS level 1b data.

Figure 5-5 (Helm, et al 2007) (top) Laser scanner derived DEM and aircraft ground track computed from acquisitions during CryoVex 2004 grand tour of the Arctic. This profile is acquired from the percolation zone at T05 on the EGIG line in Greenland. (bottom) Comparison between laser scanner derived DEM interpolated at locations coincident with ASIRAS level 1b locations. A specialised re-tracker was designed to extract the snow air interface and layer from what is believed to be the previous years melt.

6. Other Methods of Verification. In late 2004 and some months prior to the CryoSat launch a team was organised to build processing capability and analyse simulated transponder data at the level 0, a diagnostic calibrated level 1a, a diagnostic data set prior to level 1b multi-looking (termed stack data). Over the following year and beyond the CryoSat launch failure, many simulations were analyses and results from the studies undertaken are provided in Bouzinac, et al, 2006. Three teams were put together to look at simulations at key stages of processing (level 0, 1A, stack data output and level 1b). These studies mainly covered analysis of phase and range history from transponder simulations and will be extended for CryoSat-2 and may include investigations of the ASIRAS corner reflector responses.

7. Applying the Method for Sentinel-3. The mission requirements for Sentinel-3 (Drinkwater and Rebhan, 2007) are centred on oceanography with other primary concerns being sea-ice and inland water. As a secondary objective land ice surfaces will be covered. Following phase A and B1 studies there are strong similarities with SIRAL. Named SRAL the instrumentation has similar timing to SIRAL contains a SAR mode but no interferometric SAR mode. In principle the methods described in this paper to process to level 1b are applicable however one cannot rule out implementation differences that may due to the way calibration is handled and information provided in telemetry. There may also be product differences as one expects from mission to mission due to the primary objective differences. There are some other subtleties in the hardware concept that may result in differences in some algorithms and their implementation. In terms of testing one can expect some differences in the complex ocean type surfaces. Algorithm verification is possible by examination of ASIRAS data acquired over open/inland water, and sea-ice/leads.

8. Conclusions Independent means of verifying the CryoSat-2 level 1b processors are well established. The algorithms prototyped for level 1b processing in SAR and SARIn modes have been shown to be functionally correct and have been verified in part by CVRT activities over the past few years some of which is in the published literature. Of course all science processing software leaves room for improvement with knowledge gained and the CryoSat/ASIRAS level 1b processing method is not exempt from this. It has always been in the planning that with knowledge and time there will be a ‘re-spin’ of the raw CryoSat data with improved algorithms and an understanding of the real data. In the meantime, prior to CryoSat-2 launch, further methods of verification will

become apparent.

Acknowledgements The authors wish to thank all members of CVRT who contributed directly or indirectly to the generation of this paper. References [1] Bouzinac, C., Cullen, R., Reche, M. and Roca, M., ‘CryoSat level 1 data calibration with transponder’, Proceedings of the Symposium on 15 years of Progress in Radar Altimetry, ESA SP-614, Venice, Italy, 13-18 March 2006.

[2] Chelton, D. B, Walsh, E. J. and MacArthur, J. L "Pulse compression and sea level tracking in satellite altimetry", J. Atmos. Oceanic Technology, 6, 407 - 438, 1989.

[3] Curlander, J. and McDonough, R., "Synthetic aperture radar, systems and signal processing", John Wiley & Sons Inc., New York, 1991.

[4] Cullen, R. and Rey, L., 2007 “Internal calibration processing methods for ESA’s CryoSat-2 radar altimeter.”, These proceedings, EnviSat Symposium.

[5] Drinkwater, M. and Rebhan, H, 2006, “Sentinel-3 Mission requirements document”, EOP-SMO/1151/MD-md, issue 2.0, 19th Feb 2007 available on-line http://esamultimedia.esa.int/docs/GMES/GMES_Sentinel3_MRD_V2.0_update.pdf

[6] Francis, C. R., Viau, P., Cullen, R and Davidson, M. W. J., 2005, “The CryoSat data products – Their generation, in-situ validation and applications”, ESA bulletin, No. 122, May 2005.

[7] Francis, C. R., 2007, “Mission and Data Description”, CS-RP-ESA-SY-0059, issue 3. available on-line: http://esamultimedia.esa.int/docs/Cryosat/Mission_and_Data_Descrip.pdf

[8] Hawley, R. L., Morris, E. M., Cullen, R., Nixdorf, U., Shepherd, A. P and Wingham, D. J., 2006, “ASIRAS airborne radar resolves internal annual layers in the dry-snow zone of Greenland”, Geophysical Research Letters. Vol. 33. No. 4. L04502, doi:10.1029/2005GL025147.

[9] Helm, V., W. Rack, R. Cullen, P. Nienow, D. Mair, V. Parry, and D. J. Wingham (2007), “Winter accumulation in the percolation zone of Greenland measured by airborne radar altimeter”, Geophys. Res. Lett., 34, L06501, doi:10.1029/2006GL029185

[10] Jensen, J. R., “Angle measurement with a phase monopulse radar altimeter”, IEEE Trans. Antennas and Propagation, Vol. 47, No. 4, pp. 715-724, April 1999.

[11] Lentz, H., Braun, H., Younis, N., Fischer, C., Wiesbeck, W. and Mavrocordatos, C., “Concept and realisation of an airborne SAR/interferometric radar altimeter system”, in Proc. IGARSS 2002 symposium.

[12] Mavrocordatos, C., Attema, E., Davidson, M., Lenz, H. and Nixdorf, U., 2004, “Development of ASIRAS (Airborne SAR/Interferometric Altimter System)”, Proc. IGARSS 2004 Symposium.

[13] Raney, R. K., 1998, “The Delay/Doppler Radar Altimeter”, IEEE Trans. Geoscience and Remote Sensing, Vol. 36, No. 5, pp. 1578-1588 Sept 1998,

[14] Wingham, D. J., et al., 1998 “Response to the ESA Earth Explorer Opportunity Mission” AO, UCL.

[15] Wingham, D. J., 1999, “CryoSat Science and Mission Requirements” available on-line http://esamultimedia.esa.int/docs/Cryosat/MRD_21Nov99.pdf

[16] Wingham, D. J., Francis, C. R., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., de Chateau-Thierry, P., Laxon, S. W., Mallow, U., Mavrocordatos, C., Phalippou, L., Ratier, G., Rey, L., Rostan, F., Viau, P. and Wallis, D., 2006, “CryoSat: A Mission to Determine the Fluctuations in Earth's Land and Marine Ice Fields”, Advances in Space Research, Vol. 36, pp. 841-871. doi:10.1016/j.asr.2005.07.027

[17] Wingham, D. J., Phalippou. L, Mavrocordatos, C. and Wallis, D., 2004 “The mean echo and echo cross-product from a beam forming, interferometric altimeter and their application to elevation measurement”, IEEE Trans. Geoscience and Remote Sensing, Vol. 42, No. 10, pp. 2305-2323.