Sara Fleury | University of Toulouse (original) (raw)

Papers by Sara Fleury

This paper presents and compares some very interesting and promising methods to retrieve the Snow... more This paper presents and compares some very interesting and promising methods to retrieve the Snow Depth (SD) with AMSR-2. Such studies are very important because the Snow Depth over sea ice remains largely unknown whereas it plays an important role in the climate (albedo), the sea ice dynamics (thermal insulation, melt pounds), the biochemical (UV insulation), etc. But the validation of the emerging solutions is a very difficult task du to the snow diversity and the lack of in-situ data. Also we must be very careful in our conclusions and clearly stated the uncertainties and the conditions of applicability.

EGU General Assembly Conference Abstracts, Apr 1, 2015

AGU Fall Meeting Abstracts, Dec 1, 2018

Advances in Space Research, Jul 1, 2021

Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard esti... more Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+.

The Cryosphere, Jul 25, 2023

Sea ice volume's significant interannual variability requires long-term series of observations to... more Sea ice volume's significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as the Environmental Satellite (Envisat) and especially the European Remote-Sensing Satellite (ERS-1 and ERS-2) have remained under-exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic radar freeboard time series back to 1995. The difficulty in handling ERS measurements comes from a technical issue known as the pulse blurring effect, altering the radar echoes over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse blurring effect. To ensure consistency of the CryoSat-2, Envisat and ERS-2 time series, a multiparameter neuralnetwork-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the mission-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good agreement between the missions, with a mean bias of 0.30 cm and a standard deviation of 9.7 cm for Envisat and CryoSat-2 and a 0.20 cm bias and a standard deviation of 3.8 cm for ERS-2 and Envisat. The monthly corrected radar freeboards obtained from Envisat and ERS-2 are then validated by comparison with several independent datasets such as airborne, mooring, direct-measurement and other altimeter products. Except for two datasets, comparisons lead to correlations ranging from 0.41 to 0.94 for Envisat and from 0.60 to 0.74 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from the ERS-2 mission to CryoSat-2).

Sea ice volume significant interannual variability requires long-term series of observations to i... more Sea ice volume significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as Envisat and especially ERS-1 and ERS-2 have remained underexploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic freeboard time series back to 1995. The difficulty to handle ERS measurements comes from a technical issue known as the pulse-blurring effect, altering the radar echos over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse-blurring effect. To ensure consistency of the CryoSat-2/Envisat/ERS-2 time series, a multi-parameters neural network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the missions-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good consistencies between missions, with a mean bias of 3 mm for Envisat/CryoSat-2 and 2 mm for ERS-2/Envisat. The monthly maps obtained from Envisat and ERS-2 are then validated by comparison with several independent data such as airborne, moorings, direct measurements and other altimeter products. Except for two data sets, comparisons lead to correlation ranging from 0.42 to 0.94 for Envisat, and 0.6 to 0.76 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from ERS-2 mission to CryoSat-2).

Remote Sensing of Environment, Dec 1, 2016

Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investig... more Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investigated using a theoretical model and snow grain observations obtained during previous field campaigns. Our results show that the combination of radar altimeters operating at these two frequencies should allow for the retrieval of snow depth over Arctic sea ice. We estimate uncertainties of the ice surface position in relation to crossover observations over sea ice and show that the accuracy of the crossover methodology with short time gap (3 days or less) is better than 3 cm. Comparison of the CryoSat-2/AltiKa retrieved snow depth with in situ measurements provided by Operation IceBridge shows a good agreement with a Root Mean Square Error (RMSE) of 5 cm. Analysis of the CryoSat-2/AltiKa retrieved snow depths over three winters (2013–2015) reveals a thinner snow cover on both Multi-Year (32%–57%) and First-Year Ice (63%–75%) relative to the 1954–91 Warren climatology, suggesting the need for more contemporary year-round and basin-scale snow depth fields.

ESA Living Planet Symposium, Dec 1, 2013

EGU General Assembly Conference Abstracts, Apr 1, 2015

This paper presents and compares some very interesting and promising methods to retrieve the Snow... more This paper presents and compares some very interesting and promising methods to retrieve the Snow Depth (SD) with AMSR-2. Such studies are very important because the Snow Depth over sea ice remains largely unknown whereas it plays an important role in the climate (albedo), the sea ice dynamics (thermal insulation, melt pounds), the biochemical (UV insulation), etc. But the validation of the emerging solutions is a very difficult task du to the snow diversity and the lack of in-situ data. Also we must be very careful in our conclusions and clearly stated the uncertainties and the conditions of applicability.

Sea ice leads play an essential role in ocean-ice-atmosphere exchange, in ocean circulation, geoc... more Sea ice leads play an essential role in ocean-ice-atmosphere exchange, in ocean circulation, geochemistry, and in ice dynamics. Their precise detection is crucial for altimetric estimations of sea ice thickness and volume. This study evaluates the performance of the SARAL/AltiKa (Satellite with ARgos and ALtiKa) altimeter to detect leads and to monitor their spatio-temporal dynamics. We show that a pulse peakiness parameter (PP) used to detect leads by Envisat RA-2 and ERS-1,-2 altimeters is not suitable because of saturation of AltiKa return echoes over the leads. The signal saturation results in loss of 6–10% of PP data over sea ice. We propose a different parameter—maximal power of waveform—and define the threshold to discriminate the leads. Our algorithm can be applied from December until May. It detects well the leads of small and medium size from 200 m to 3–4 km. So the combination of the high-resolution altimetric estimates with low-resolution thermal infra-red or radiometric...

<p>It is well established that winter and spring Arctic sea-ice thi... more <p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente d... more L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et le...

<p>Thanks to the relatively high inclinatio... more <p>Thanks to the relatively high inclination (81.5°N/S) of the ERS2, Envisat, CryoSat-2, Saral and S3 space altimeters, the Polar Regions have been observed continuously by radar altimetry since the 1990s. We thus have time series over nearly 30 years of the topography of the polar ice caps and the thickness of the ice pack.  However, these measurements took a qualitative leap forward with the launch of CryoSat-2 in 2010, thanks to the advent of SAR/SARIN altimetry and a near-polar inclination of 88°N/S.</p><p>SAR/SARIN altimetry has led to considerable improvements in measurement accuracy thanks to better focusing (reducing the footprint by a factor of about 100) and better resolution (by a factor of about 2). The inclination of 88°N/S provides us with almost complete coverage of the Polar Regions, enabling us to carry out 10-year assessments of polar caps and sea-ice volume variations.</p><p>During this presentation, we will first show the many scientific advances made possible by polar altimetry and its various evolutions, including the high-precision lidar solution on board NASA's IceSat-2 satellite.</p><p>We will then present the HPCM CRISTAL mission, the only new polar altimetry mission planned to date.  We will see the technical advances proposed by this mission and its importance in monitoring the Polar Regions in the context of global warming.</p>

Advances in Space Research, 2020

Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard esti... more Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+.

Remote Sensing of Environment, 2016

Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investig... more Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investigated using a theoretical model and snow grain observations obtained during previous field campaigns. Our results show that the combination of radar altimeters operating at these two frequencies should allow for the retrieval of snow depth over Arctic sea ice. We estimate uncertainties of the ice surface position in relation to crossover observations over sea ice and show that the accuracy of the crossover methodology with short time gap (3 days or less) is better than 3 cm. Comparison of the CryoSat-2/AltiKa retrieved snow depth with in situ measurements provided by Operation IceBridge shows a good agreement with a Root Mean Square Error (RMSE) of 5 cm. Analysis of the CryoSat-2/AltiKa retrieved snow depths over three winters (2013–2015) reveals a thinner snow cover on both Multi-Year (32%–57%) and First-Year Ice (63%–75%) relative to the 1954–91 Warren climatology, suggesting the need for more contemporary year-round and basin-scale snow depth fields.

This paper presents and compares some very interesting and promising methods to retrieve the Snow... more This paper presents and compares some very interesting and promising methods to retrieve the Snow Depth (SD) with AMSR-2. Such studies are very important because the Snow Depth over sea ice remains largely unknown whereas it plays an important role in the climate (albedo), the sea ice dynamics (thermal insulation, melt pounds), the biochemical (UV insulation), etc. But the validation of the emerging solutions is a very difficult task du to the snow diversity and the lack of in-situ data. Also we must be very careful in our conclusions and clearly stated the uncertainties and the conditions of applicability.

EGU General Assembly Conference Abstracts, Apr 1, 2015

AGU Fall Meeting Abstracts, Dec 1, 2018

Advances in Space Research, Jul 1, 2021

Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard esti... more Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+.

The Cryosphere, Jul 25, 2023

Sea ice volume's significant interannual variability requires long-term series of observations to... more Sea ice volume's significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as the Environmental Satellite (Envisat) and especially the European Remote-Sensing Satellite (ERS-1 and ERS-2) have remained under-exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic radar freeboard time series back to 1995. The difficulty in handling ERS measurements comes from a technical issue known as the pulse blurring effect, altering the radar echoes over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse blurring effect. To ensure consistency of the CryoSat-2, Envisat and ERS-2 time series, a multiparameter neuralnetwork-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the mission-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good agreement between the missions, with a mean bias of 0.30 cm and a standard deviation of 9.7 cm for Envisat and CryoSat-2 and a 0.20 cm bias and a standard deviation of 3.8 cm for ERS-2 and Envisat. The monthly corrected radar freeboards obtained from Envisat and ERS-2 are then validated by comparison with several independent datasets such as airborne, mooring, direct-measurement and other altimeter products. Except for two datasets, comparisons lead to correlations ranging from 0.41 to 0.94 for Envisat and from 0.60 to 0.74 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from the ERS-2 mission to CryoSat-2).

Sea ice volume significant interannual variability requires long-term series of observations to i... more Sea ice volume significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as Envisat and especially ERS-1 and ERS-2 have remained underexploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic freeboard time series back to 1995. The difficulty to handle ERS measurements comes from a technical issue known as the pulse-blurring effect, altering the radar echos over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse-blurring effect. To ensure consistency of the CryoSat-2/Envisat/ERS-2 time series, a multi-parameters neural network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the missions-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good consistencies between missions, with a mean bias of 3 mm for Envisat/CryoSat-2 and 2 mm for ERS-2/Envisat. The monthly maps obtained from Envisat and ERS-2 are then validated by comparison with several independent data such as airborne, moorings, direct measurements and other altimeter products. Except for two data sets, comparisons lead to correlation ranging from 0.42 to 0.94 for Envisat, and 0.6 to 0.76 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from ERS-2 mission to CryoSat-2).

Remote Sensing of Environment, Dec 1, 2016

Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investig... more Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investigated using a theoretical model and snow grain observations obtained during previous field campaigns. Our results show that the combination of radar altimeters operating at these two frequencies should allow for the retrieval of snow depth over Arctic sea ice. We estimate uncertainties of the ice surface position in relation to crossover observations over sea ice and show that the accuracy of the crossover methodology with short time gap (3 days or less) is better than 3 cm. Comparison of the CryoSat-2/AltiKa retrieved snow depth with in situ measurements provided by Operation IceBridge shows a good agreement with a Root Mean Square Error (RMSE) of 5 cm. Analysis of the CryoSat-2/AltiKa retrieved snow depths over three winters (2013–2015) reveals a thinner snow cover on both Multi-Year (32%–57%) and First-Year Ice (63%–75%) relative to the 1954–91 Warren climatology, suggesting the need for more contemporary year-round and basin-scale snow depth fields.

ESA Living Planet Symposium, Dec 1, 2013

EGU General Assembly Conference Abstracts, Apr 1, 2015

This paper presents and compares some very interesting and promising methods to retrieve the Snow... more This paper presents and compares some very interesting and promising methods to retrieve the Snow Depth (SD) with AMSR-2. Such studies are very important because the Snow Depth over sea ice remains largely unknown whereas it plays an important role in the climate (albedo), the sea ice dynamics (thermal insulation, melt pounds), the biochemical (UV insulation), etc. But the validation of the emerging solutions is a very difficult task du to the snow diversity and the lack of in-situ data. Also we must be very careful in our conclusions and clearly stated the uncertainties and the conditions of applicability.

Sea ice leads play an essential role in ocean-ice-atmosphere exchange, in ocean circulation, geoc... more Sea ice leads play an essential role in ocean-ice-atmosphere exchange, in ocean circulation, geochemistry, and in ice dynamics. Their precise detection is crucial for altimetric estimations of sea ice thickness and volume. This study evaluates the performance of the SARAL/AltiKa (Satellite with ARgos and ALtiKa) altimeter to detect leads and to monitor their spatio-temporal dynamics. We show that a pulse peakiness parameter (PP) used to detect leads by Envisat RA-2 and ERS-1,-2 altimeters is not suitable because of saturation of AltiKa return echoes over the leads. The signal saturation results in loss of 6–10% of PP data over sea ice. We propose a different parameter—maximal power of waveform—and define the threshold to discriminate the leads. Our algorithm can be applied from December until May. It detects well the leads of small and medium size from 200 m to 3–4 km. So the combination of the high-resolution altimetric estimates with low-resolution thermal infra-red or radiometric...

<p>It is well established that winter and spring Arctic sea-ice thi... more <p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente d... more L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et le...

<p>Thanks to the relatively high inclinatio... more <p>Thanks to the relatively high inclination (81.5°N/S) of the ERS2, Envisat, CryoSat-2, Saral and S3 space altimeters, the Polar Regions have been observed continuously by radar altimetry since the 1990s. We thus have time series over nearly 30 years of the topography of the polar ice caps and the thickness of the ice pack.  However, these measurements took a qualitative leap forward with the launch of CryoSat-2 in 2010, thanks to the advent of SAR/SARIN altimetry and a near-polar inclination of 88°N/S.</p><p>SAR/SARIN altimetry has led to considerable improvements in measurement accuracy thanks to better focusing (reducing the footprint by a factor of about 100) and better resolution (by a factor of about 2). The inclination of 88°N/S provides us with almost complete coverage of the Polar Regions, enabling us to carry out 10-year assessments of polar caps and sea-ice volume variations.</p><p>During this presentation, we will first show the many scientific advances made possible by polar altimetry and its various evolutions, including the high-precision lidar solution on board NASA's IceSat-2 satellite.</p><p>We will then present the HPCM CRISTAL mission, the only new polar altimetry mission planned to date.  We will see the technical advances proposed by this mission and its importance in monitoring the Polar Regions in the context of global warming.</p>

Advances in Space Research, 2020

Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard esti... more Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+.

Remote Sensing of Environment, 2016

Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investig... more Abstract The scattering properties of the radar signal at Ka and Ku-band frequencies are investigated using a theoretical model and snow grain observations obtained during previous field campaigns. Our results show that the combination of radar altimeters operating at these two frequencies should allow for the retrieval of snow depth over Arctic sea ice. We estimate uncertainties of the ice surface position in relation to crossover observations over sea ice and show that the accuracy of the crossover methodology with short time gap (3 days or less) is better than 3 cm. Comparison of the CryoSat-2/AltiKa retrieved snow depth with in situ measurements provided by Operation IceBridge shows a good agreement with a Root Mean Square Error (RMSE) of 5 cm. Analysis of the CryoSat-2/AltiKa retrieved snow depths over three winters (2013–2015) reveals a thinner snow cover on both Multi-Year (32%–57%) and First-Year Ice (63%–75%) relative to the 1954–91 Warren climatology, suggesting the need for more contemporary year-round and basin-scale snow depth fields.