Sentences with phrase «of ice thickness data»

Johannes Fürst, a researcher at the University of Erlangen - Nuremberg's Institute of Geography in Germany, and colleagues report in Nature Climate Change that they analysed years of ice thickness data from European Space Agency satellites and airborne measurements.

To establish this uncertainty in the ice - volume record (Schweiger et al. 2011), we spent a significant effort drawing on most types of available observations of ice thickness thanks to a convenient compilation of ice thickness data (Lindsay, 2010).

Less is known about southwest Greenland glaciers due to a lack of ice thickness data but the glaciers have accelerated there as well and are likely to be strongly out of balance despite thickening of the interior.

Not exact matches

Morris uses the information she gathers on these trips to check the accuracy of data collected by a European satellite, Cryosat - 2, that tracks changes in the thickness of polar ice — information that tells scientists how quickly that ice is thawing.

The study uses data from two NASA missions — Operation IceBridge, which measures ice thickness and gravity from aircraft, and Oceans Melting Greenland, or OMG, which uses sonar and gravity instruments to map the shape and depth of the seafloor close to the ice front.

Scientists used the data to explore Antarctic ice thickness and the distribution of subglacial lakes.

Initial interpretations of data from Cassini flybys of Enceladus estimated that the thickness of its ice shell ranged from 30 to 40 km at the south pole to 60 km at the equator.

The scientists made this projection after evaluating current satellite data about the thickness of the ice cover.

The researchers combined data gathered from the buoys between 2002 and 2015 with satellite estimates of ice thickness in this region to better understand changes affecting the Arctic Ocean in recent years.

If everything goes according to plan, the radar will be turned on and will start to collect data on the thickness of glaciers and ice sheets just three days post-launch.

The lack of many kinds of data — high - resolution topography and bathymetry along the coasts; measurements of ice cover and thickness; distributions in space and time of the region's fish, birds, and marine mammals — is another.

Analysis of the data showed that despite isolated cases where ice volume and thickness increased, none of the advancing glaciers have come close to the maximums achieved during the so - called «Little Ice Age» — a period of cooling between the sixteenth and the nineteenth centuice volume and thickness increased, none of the advancing glaciers have come close to the maximums achieved during the so - called «Little Ice Age» — a period of cooling between the sixteenth and the nineteenth centuIce Age» — a period of cooling between the sixteenth and the nineteenth century.

We calculate the WD gas age - ice age difference (Delta age) using a combination of firn densification modeling, ice - flow modeling, and a data set of d15N - N2, a proxy for past firn column thickness.

In order to test their hypothesis, the researchers would need more data regarding the eccentricity of Charon's orbit, as well as the interior structure of Pluto and Charon and in particular the thickness, strength, and viscosity of the latter's underground ice shell, which are currently unavailable.

First, we expect the ice thickness distribution in April 30 from redistribution (divergence / convergence) of sea ice during December and April, based on the daily ice velocity data.

Finnish Meteorological Institute has been doing estimates of two essential sea ice parameters — namely, sea ice concentration (SIC) and sea ice thickness (SIT)-- for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer.

At FMI algorithms and procedures have been developed for producing daily thin ice thickness (< 0.5 m) charts for the Arctic in wintertime based on ice surface temperature which is retrieved from the thermal infrared data of the MODIS spectrometer.

Zhang, J., D. R. Thomas, D. A. Rothrock, R. W. Lindsay, Y. Yu, and R. Kwok (2003), Assimilation of ice motion observations and comparisons with submarine ice thickness data, J.Geophys.Res., 108 (C6), 3170, DOI: 3110.1029 / 2001JC001041 Zhang, J., and D. A. Rothrock (2003), Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Monthly Weather Review, 131 (5), 845 - 861.

«The skill of the model is examined by comparing its output to sea ice thickness data gathered during the last two decades.

Decadal hindcast simulations of Arctic Ocean sea ice thickness made by a modern dynamic - thermodynamic sea ice model and forced independently by both the ERA - 40 and NCEP / NCAR reanalysis data sets are compared for the first time.

Using comprehensive data sets of observations made between 1979 and 2001 of sea ice thickness, draft, extent, and speeds, we find that it is possible to tune model parameters to give satisfactory agreement with observed data, thereby highlighting the skill of modern sea ice models, though the parameter values chosen differ according to the model forcing used.

Professor Peter Wadhams, member of AMEG, expert on Arctic sea ice and a reviewer for the IPCC AR5 report, says that the PIOMAS data is based on actual thickness measurements.

results (of direct ice thickness measurements by bore holes coupled with historic data) in October 2009, Professor Wadhams said,....

Instead, a rather casual article in the Independent showed the latest thickness data and that quoted Mark Serreze as saying that the area around the North Pole had 50/50 odds of being completely ice free this summer, has taken off across the media.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice predictors.

At FMI algorithms and procedures have been developed for producing daily thin ice thickness (< 0.5 m) charts for the Arctic in wintertime based on ice surface temperature which is retrieved from the thermal infrared data of the MODIS spectrometer.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice predictors.

So both the RN and USN have a LOT of data on ice - pack thickness and extent.

The most recent ice data, 10 June 2013, from a SAMS ice mass balance buoy installed in the fast ice in Inglefieldbukta (N 77 ° 54», E 18 ° 17») reported an ice thickness of about 88 cm and snow depth 20 cm.

Zhang, J., D.R. Thomas, D.A. Rothrock, R.W. Lindsay, Y. Yu, and R. Kwok, «Assimilation of ice motion observations and comparisons with submarine ice thickness data ``, J. Geophys.

IceBridge data are collected from aircraft that fly over the ice cover carrying a suite of instruments, including altimeters that can directly measure ice thickness above the surface.

Scientists from the University of Erlangen - Nuremberg Institute of Geography and from the Laboratoire de Glaciologie et Gophysique de l'Environnement in Grenoble, France, used radar data from satellites such as ESA's Envisat and observations of ice thickness from airborne surveys in a complex model to demonstrate, for the first time, how the buttressing role of the ice shelves is being compromised as the shelves decline.

Given the minimum ice extent is about 4 million km2 and 4000 km3 (an average of 1 meter thickness) the SMOS data is of limited value.

The optical thickness for Santa Maria (0.55 times that of Pinatubo) has comparable aerosol amount in both hemispheres based on ice core data.

Researchers used data from IceBridge's ice - penetrating radar — the Multichannel Coherent Radar Depth Sounder, or MCoRDS, which is operated by the Center for Remote Sensing of Ice Sheets at the University of Kansas, Lawrence, Kan. — to determine ice thickness and sub-glacial terrain, and images from satellite sources such as Landsat and Terra to calculate velociice - penetrating radar — the Multichannel Coherent Radar Depth Sounder, or MCoRDS, which is operated by the Center for Remote Sensing of Ice Sheets at the University of Kansas, Lawrence, Kan. — to determine ice thickness and sub-glacial terrain, and images from satellite sources such as Landsat and Terra to calculate velociIce Sheets at the University of Kansas, Lawrence, Kan. — to determine ice thickness and sub-glacial terrain, and images from satellite sources such as Landsat and Terra to calculate velociice thickness and sub-glacial terrain, and images from satellite sources such as Landsat and Terra to calculate velocity.

One of the important ingredients of the new model is data on the thickness of ice floating on the sea.

• Expand our existing Unified Sea Ice Thickness Climate Data Record (Sea Ice CDR) to include ICESat, IceBridge, and CryoSat - 2 estimates of the ice thickneIce Thickness Climate Data Record (Sea Ice CDR) to include ICESat, IceBridge, and CryoSat - 2 estimates of the ice tThickness Climate Data Record (Sea Ice CDR) to include ICESat, IceBridge, and CryoSat - 2 estimates of the ice thickneIce CDR) to include ICESat, IceBridge, and CryoSat - 2 estimates of the ice thickneice thicknessthickness.

The ensemble consists of seven members each of which uses a unique set of NCEP / NCAR atmospheric forcing fields from recent years, representing recent climate, such that ensemble member 1 uses 2005 NCEP / NCAR forcing, member 2 uses 2006 forcing..., and member 7 uses 2011 forcing... In addition, the recently available IceBridge and helicopter - based electromagnetic (HEM) ice thickness quicklook data are assimilated into the initial 12 - category sea ice thickness distribution fields in order to improve the initial conditions for the predictions.

Here, thickness data, which are sorely lacking but available in a few locations as the result of International Polar Year efforts and from satellite - derived estimates of ice age or type, constrain modeled thickness distributions.

Starting with the April Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) volume distribution and the April National Snow and Ice Data Center (NSIDC) average ice extent the estimated extent loss for each 10 cm thickness of ice loss is calculatIce Ocean Modeling and Assimilation System (PIOMAS) volume distribution and the April National Snow and Ice Data Center (NSIDC) average ice extent the estimated extent loss for each 10 cm thickness of ice loss is calculatIce Data Center (NSIDC) average ice extent the estimated extent loss for each 10 cm thickness of ice loss is calculatice extent the estimated extent loss for each 10 cm thickness of ice loss is calculatice loss is calculated.

What is needed is a more systematic way of integrating data on the thickness distribution of this ice into models that forecast regional ice conditions and their impact on ice ocean interaction.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoIce Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice predictors.

The Outlook also underscored important lessons for improvements in future efforts, including: a need for additional work on remote sensing of spring and summer sea ice conditions; sea ice thickness data; and more formal forecasting and evaluation methods.

Ice - ocean model simulations, on the other hand, have requirements with respect to data density and quality, e.g., for observed ice thickness fields used in initialization of model runs, that are currently not being met by existing data sources (with the exception of, e.g., satellite - observed ice concentration fieldIce - ocean model simulations, on the other hand, have requirements with respect to data density and quality, e.g., for observed ice thickness fields used in initialization of model runs, that are currently not being met by existing data sources (with the exception of, e.g., satellite - observed ice concentration fieldice thickness fields used in initialization of model runs, that are currently not being met by existing data sources (with the exception of, e.g., satellite - observed ice concentration fieldice concentration fields).

Other in situ and satellite data suggest that even though the seasonal ice cover was formed later in the fall of 2007, the mean thickness of first year ice cover is comparable to that of the previous two seasons because of lower snow accumulation and lower air temperatures and thus, faster growth.

As a result of limited satellite observations of sea ice thickness (for more information: Sea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thickneice thickness (for more information: Sea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice tthickness (for more information: Sea Ice Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thickneIce Thickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice tThickness Data Sets: Overview and Comparison), few climate modeling experiments have isolated the role of changing sea ice thickneice thicknessthickness.

Kaleschke and Rickert provided an estimate of the difference between March 2013 and March 2012 ice thickness based on preliminary data from the European Space Agency's satellites CryoSat - 2 and SMOS (Figure 6).

As in 2012, sea ice thinning and not just anomalous weather should contribute to September 2013 sea ice loss (see the discussion of the IceBridge sea ice thickness data from the June Report).

To determine how much ice and snowfall enters a specific ice shelf and how much makes it to an iceberg, where it may split off, the research team used a regional climate model for snow accumulation and combined the results with ice velocity data from satellites, ice shelf thickness measurements from NASA's Operation IceBridge — a continuing aerial survey of Earth's poles — and a new map of Antarctica's bedrock.

These missions - satellite radar altimetry projects overseen by the European Space Agency (ESA)- lasted from 1994 to 2012, providing the researchers plenty of data that could even be overlapped and compared to ensure an accurate assessment of ice shelf thickness for more than a decade.