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Brucker, Ludovic

Publications and source records attributed to Brucker, Ludovic.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Physical Models of Layered Polar Firn Brightness Temperatures from 0.5 to 2 GHz

We investigate physical effects influencing 0.5-2 GHz brightness temperatures of layered polar firn to support the Ultra Wide Band Software Defined Radiometer (UWBRAD) experiment to be conducted in Greenland and in Antarctica. We find that because ice particle grain sizes are very small compared to the 0.5-2 GHz wavelengths, volume scattering effects are small. Variations in firn density over cm- to m-length scales, however, cause significant effects. Both incoherent and coherent models are used to examine these effects. Incoherent models include a 'cloud model' that neglects any reflections internal to the ice sheet, and the DMRT-ML and MEMLS radiative transfer codes that are publicly available. The coherent model is based on the layered medium implementation of the fluctuation dissipation theorem for thermal microwave radiation from a medium having a nonuniform temperature. Density profiles are modeled using a stochastic approach, and model predictions are averaged over a large number of realizations to take into account an averaging over the radiometer footprint. Density profiles are described by combining a smooth average density profile with a spatially correlated random process to model density fluctuations. It is shown that coherent model results after ensemble averaging depend on the correlation lengths of the vertical density fluctuations. If the correlation length is moderate or long compared with the wavelength (approximately 0.6x longer or greater for Gaussian correlation function without regard for layer thinning due to compaction), coherent and incoherent model results are similar (within approximately 1 K). However, when the correlation length is short compared to the wavelength, coherent model results are significantly different from the incoherent model by several tens of kelvins. For a 10-cm correlation length, the differences are significant between 0.5 and 1.1 GHz, and less for 1.1-2 GHz. Model results are shown to be able to match the v-pol SMOS data closely and predict the h-pol data for small observation angles.

layered polar firn emission↗
NASA NTRS2014

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer), and is available at http:lgge.osug.frpicarddmrtml.

ice sheet↗
NASA NTRS2014

Simulation of Seasonal Snow Microwave TB Using Coupled Multi-Layered Snow Evolution and Microwave Emission Models

The accurate quantification of SWE has important societal benefits, including improving domestic and agricultural water planning, flood forecasting and electric power generation. However, passive-microwave SWE algorithms suffer from variations in TB due to snow metamorphism, difficult to distinguish from those due to SWE variations. Coupled snow evolution-emission models are able to predict snow metamorphism, allowing us to account for emissivity changes. They can also be used to identify weaknesses in the snow evolution model. Moreover, thoroughly evaluating coupled models is a contribution toward the assimilation of TB, which leads to a significant increase in the accuracy of SWE estimates.

microwave↗
NASA NTRS2014

Aquarius Radiometer and Scatterometer Weekly-Polar-Gridded Products to Monitor Ice Sheets, Sea Ice, and Frozen Soil

Space-based microwave sensors have been available for several decades, and with time more frequencies have been offered. Observations made at frequencies between 7 and 183 GHz were often used for monitoring cryospheric properties (e.g. sea ice concentration, snow accumulation, snow melt extent and duration). Since 2009, satellite observations are available at the low frequency of 1.4 GHz. Such observations are collected by the Soil Moisture and Ocean Salinity (SMOS) mission, and the AquariusSAC-D mission. Even though these missions have been designed for the monitoring of soil moisture and sea surface salinity, new applications are being developed to study the cryosphere. For instance, L-band observations can be used to monitor soil freezethaw (e.g. Rautiainen et al., 2012), and thin sea ice thickness (e.g. Kaleschke et al., 2010, Huntemann et al., 2013). Moreover, with the development of satellite missions comes the need for calibration and validation sites. These sites must have stable characteristics, such as the Antarctic Plateau (Drinkwater et al., 2004, Macelloni et al., 2013). Therefore, studying the cryosphere with 1.4 GHz observations is relevant for both science applications, and remote sensing applications.

Aquarius↗
NASA NTRS2014

L-Band Brightness Temperature Variations at Dome C and Snow Metamorphism at the Surface

The Antarctic Plateau is a promising site to monitor microwave radiometers' drift, and to inter-calibrate microwave radiometers, especially 1.4 GigaHertz (L-band) radiometers on board the Soil Moisture and Ocean Salinity (SMOS), and AquariusSAC-D missions. The Plateau is a thick ice cover, thermally stable in depth, with large dimensions, and relatively low heterogeneities. In addition, its high latitude location in the Southern Hemisphere enables frequent observations by polar-orbiting satellites, and no contaminations by radio frequency interference. At Dome C (75S, 123E), on the Antarctic Plateau, the substantial amount of in-situ snow measurements available allows us to interpret variations in space-borne microwave brightness temperature (TB) (e.g. Macelloni et al., 2007, 2013, Brucker et al., 2011, Champollion et al., 2013). However, to analyze the observations from the Aquarius radiometers, whose sensitivity is 0.15 K, the stability of the snow layers near the surface that are most susceptible to rapidly change needs to be precisely assessed. This study focuses on the spatial and temporal variations of the Aquarius TB over the Antarctic Plateau, and at Dome C in particular, to highlight the impact of snow surface metamorphism on the TB observations at L-band.

l-band↗
NASA NTRS2014

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer).

Microwave radiometer↗
NASA NTRS2014

Aquarius Radiometer and Scatterometer Weekly Polar-Gridded Products to Monitor Ice Sheets, Sea Ice, and Frozen Soil

Space-based microwave sensors have been available for several decades, and with time more frequencies have been offered. Observations made at frequencies between 7 and 183 GHz were often used for monitoring cryospheric properties (e.g. sea ice concentration, snow accumulation, snow melt extent and duration). Since 2009, satellite observations are available at the low frequency of 1.4 GHz. Such observations are collected by the Soil Moisture and Ocean Salinity (SMOS) mission, and the Aquarius/SAC-D mission. Even though these missions have been designed for the monitoring of soil moisture and sea surface salinity, new applications are being developed to study the cryosphere. For instance, L-band observations can be used to monitor soil freeze/thaw (e.g. Rautiainen et al., 2012), and thin sea ice thickness (e.g. Kaleschke et al., 2010, Huntemann et al., 2013). Moreover, with the development of satellite missions comes the need for calibration and validation sites. These sites must have stable characteristics, such as the Antarctic Plateau (Drinkwater et al., 2004, Macelloni et al., 2013). Therefore, studying the cryosphere with 1.4 GHz observations is relevant for both science applications, and remote sensing applications.

Sea ice↗
NASA NTRS2014

Aquarius Brightness Temperature Variations at Dome C and Snow Metamorphism at the Surface

The Antarctic Plateau is a promising site to monitor microwave radiometers' drift, and to inter-calibrate microwave radiometers, especially 1.4 GHz (L-band) radiometers on board the Soil Moisture and Ocean Salinity (SMOS), and AquariusSAC-D missions. The Plateau is a thick ice cover, thermally stable in depth, with large dimensions, and relatively low heterogeneities. In addition, its high latitude location in the Southern Hemisphere enables frequent observations by polar-orbiting satellites, and no contaminations by radio frequency interference. At Dome C (75S, 123E), on the Antarctic Plateau, the substantial amount of in-situ snow measurements available allows us to interpret variations in space-borne microwave brightness temperature (TB) (e.g. Macelloni et al., 2007, 2013, Brucker et al., 2011, Champollion et al., 2013). However, to analyze the observations from the Aquarius radiometers, whose sensitivity is 0.15 K, the stability of the snow layers near the surface that are most susceptible to rapidly change needs to be precisely assessed. This study focuses on the spatial and temporal variations of the Aquarius TB over the Antarctic Plateau, and at Dome C in particular, to highlight the impact of snow surface metamorphism on the TB observations at L-band.

l-band↗
NASA NTRS2014

Spatio-Temporal Video Segmentation with Shape Growth or Shrinkage Constraint

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

Segmentation↗
NASA NTRS2014

Weekly Gridded Aquarius L-band Radiometer-scatterometer Observations and Salinity Retrievals over the Polar Regions - Part 1: Product Description

Passive and active observations at L band (frequency (is) approximately 1.4 GHz) from the Aquarius/SAC-D mission offer new capabilities to study the polar regions. Due to the lack of polar-gridded products, however, applications over the cryosphere have been limited. We present three weekly polar-gridded products of Aquarius data to improve our understanding of L-band observations of ice sheets, sea ice, permafrost, and the polar oceans. Additionally, these products intend to facilitate access to L-band data, and can be used to assist in algorithm developments. Aquarius data at latitudes higher than 50 degrees are averaged and gridded into weekly products of brightness temperature (TB), normalized radar cross section (NRCS), and sea surface salinity (SSS). Each grid cell also contains sea ice fraction, the standard deviation of TB, NRCS, and SSS, and the number of footprint observations collected during the seven-day cycle. The largest 3 dB footprint dimensions are 97 km×156 km and 74 km×122 km (along × across track) for the radiometers and scatterometer, respectively. The data is gridded to the Equal-Area Scalable Earth version 2.0 (EASE2.0) grid, with a grid cell resolution of 36 km. The data sets start in August 2011, with the first Aquarius observations and will be updated on a monthly basis following the release schedule of the Aquarius Level 2 data sets. The weekly gridded products are distributed by the US National Snow and Ice Data Center at http://nsidc.org/data/aquarius/index.html

Aquarius↗
NASA NTRS2014

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

Sea Ice↗
NASA NTRS2014

The Impact of the Assimilation of Aquarius Sea Surface Salinity Data in the GEOS Ocean Data Assimilation System

Ocean salinity and temperature differences drive thermohaline circulations. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius alongtrack retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with insitu salinity observations from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in-situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in-situ (Argo) and space-borne surface (SSS) observations

Salinty↗
NASA NTRS2014

Initial in Situ Measurements of Perennial Meltwater Storage in the Greenland Firn Aquifer

A perennial storage of water in a firn aquifer was discovered in southeast Greenland in 2011. We present the first in situ measurements of the aquifer, including densities and temperatures. Water was present at depths between approx. 12 and 37m and amounted to 18.7 +/- 0.9 kg in the extracted core. The water filled the firn to capacity at approx. 35m. Measurements show the aquifer temperature remained at the melting point, representing a large heat reservoir within the firn. Using model results of liquid water extent and aquifer surface depth from radar measurements, we extend our in situ measurements to the Greenland ice sheet. The estimated water volume is 140 +/- 20 Gt, representing approx. 0.4mm of sea level rise (SLR). It is unknown if the aquifer temporary buffers SLR or contributes to SLR through drainage and/or ice dynamics.

measurements↗
NASA NTRS2013

Shape-Constrained Segmentation Approach for Arctic Multiyear Sea Ice Floe Analysis

The melting of sea ice is correlated to increases in sea surface temperature and associated climatic changes. Therefore, it is important to investigate how rapidly sea ice floes melt. For this purpose, a new Tempo Seg method for multi temporal segmentation of multi year ice floes is proposed. The microwave radiometer is used to track the position of an ice floe. Then,a time series of MODIS images are created with the ice floe in the image center. A Tempo Seg method is performed to segment these images into two regions: Floe and Background.First, morphological feature extraction is applied. Then, the central image pixel is marked as Floe, and shape-constrained best merge region growing is performed. The resulting tworegionmap is post-filtered by applying morphological operators.We have successfully tested our method on a set of MODIS images and estimated the area of a sea ice floe as afunction of time.

SEA ICE FLOE↗