Engineering topics

Markus, Thorsten

Publications and source records attributed to Markus, Thorsten.

At least 19 records

NASA NTRS2019

Antarctic Ice Shelf Thickness Change from Multimission Lidar Mapping

We calculate rates of ice thickness change and bottom melt for ice shelves in West Antarctica and the Antarctic Peninsula from a combination of elevation measurements from NASA–CECS Antarctic ice mapping campaigns and NASA Operation IceBridge corrected for oceanic processes from measurements and models, surface velocity measurements from synthetic aperture radar, and high-resolution outputs from regional climate models. The ice thickness change rates are calculated in a Lagrangian reference frame to reduce the effects from advection of sharp vertical features, such as cracks and crevasses, that can saturate Eulerian-derived estimates. We use our method over different ice shelves in Antarctica, which vary in terms of size, repeat coverage from airborne altimetry, and dominant processes governing their recent changes. We find that the Larsen-C Ice Shelf is close to steady state over our observation period with spatial variations in ice thickness largely due to the flux divergence of the shelf. Firn and surface processes are responsible for some short-term variability in ice thickness of the Larsen-C Ice Shelf over the time period. The Wilkins Ice Shelf is sensitive to short-timescale coastal and upper-ocean processes, and basal melt is the dominant contributor to the ice thickness change over the period. At the Pine Island Ice Shelf in the critical region near the grounding zone, we find that ice shelf thickness change rates exceed 40m/yr, with the change dominated by strong submarine melting. Regions near the grounding zones of the Dotson and Crosson ice shelves are decreasing in thickness at rates greater than 40m/yr, also due to intense basal melt. NASA–CECS Antarctic ice mapping and NASA Operation IceBridge campaigns provide validation datasets for floating ice shelves at moderately high resolution when coregistered using Lagrangian methods.

Sutterley, Tyler C.↗

NASA NTRS2019

Mabel Engineering Flights, 2010-2013: Flight Report

In December 2010, NASA deployed for the first time the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne simulator for Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) algorithm development. Between 2010 and 2013, engineering flights were conducted in the continental United States, to ready the instrument for deployments to Alaska and Iceland, where flight lines could be designed and flown over sea ice and grounded ice. Ultimately, MABEL engineering missions included: 1) flights based out of NASA Armstrong Flight Research Center (California, formerly Dryden Flight Research Center) in 2010, 2011, and 2012; flights based out of NASA Wallops Flight Facility (Virginia) in 2012; flights based out of NASA Langley Research Center (Virginia) in 2013; and flights based out of the Mojave Air and Space Port (California) in 2013.

Brunt, Kelly M.↗

NASA NTRS2018

The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currentlyconfigured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean throughthe accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two(vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection_divergence, blowingsnow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim,and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus "median" daily snowfall dataset from these reanalyses.The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate_strong correlations and root mean squared errors of 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Petty, Alek A.↗

NASA NTRS2018

Intercomparison of Precipitation Estimates over the Arctic Ocean and Its Peripheral Seas from Reanalyses

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the ‘‘new Arctic’’). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.

Mcpartland, Linette N.↗

NASA NTRS2017

MABEL Iceland 2012 Flight Report

In March and April 2012, NASA conducted an airborne lidar campaign based out of Keflavik, Iceland, in support of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) algorithm development. The survey targeted the Greenland Ice Sheet, Iceland ice caps, and sea ice in the Arctic Ocean during the winter season. Ultimately, the mission, MABEL Iceland 2012, including checkout and transit flights, conducted 14 science flights, for a total of over 80 flight hours over glaciers, icefields, and sea ice.

MABEL↗

NASA NTRS2017

Improving Our Understanding of Antarctic Sea Ice with NASA's Operation IceBridge and the Upcoming ICESat-2 Mission

Antarctic sea ice is a crucial component of the global climate system. Rapid sea ice production regimes around Antarctica feed the lower branch of the Southern Ocean overturning circulation through intense brine rejection and the formation of Antarctic Bottom Water (e.g., Orsi et al. 1999; Jacobs 2004), while the northward transport and subsequent melt of Antarctic sea ice drives the upper branch of the overturning circulation through freshwater input (Abernathy et al. 2016). Wind-driven trends in Antarctic sea ice (Holland Kwok 2012) have likely increased the transport of freshwater away from the Antarctic coastline, significantly altering the salinity distribution of the Southern Ocean (Haumann et al. 2016). Conversely, weaker sea ice production and the lack of shelf water formation over the Amundsen and Bellingshausen shelf seas promote intrusion of warm Circumpolar Deep Water onto the continental shelf and the ocean-driven melting of several ice shelves fringing the West Antarctic Ice Sheet (e.g., Jacobs et al. 2011; Pritchard et al. 2012; Dutrieux et al. 2014). Sea ice conditions around Antarctica are also increasingly considered an important factor impacting local atmospheric conditions and the surface melting of Antarctic ice shelves (e.g., Scambos et al. 2017). Sea ice formation around Antarctica is responsive to the strong regional variability in atmospheric forcing present around Antarctica, driving this bimodal variability in the behavior and properties of the underlying shelf seas (e.g., Petty et al. 2012; Petty et al. 2014).

Arctic↗

NASA NTRS2017

MABEL Alaska 2014 Flight Report

In July and August 2014, NASA conducted an airborne lidar campaign based out of Fort Wainwright,Fairbanks, Alaska, in support of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) algorithm development. The survey targeted Alaskan glaciers and icefields and sea ice in the Arctic Ocean during the summer melt season. Ultimately, the mission, MABEL Alaska 2014, including checkout and transit flights, conducted 11 science flights, for a total of over 50 flight hours over glaciers, icefields, and sea ice.

Alaska↗

NASA NTRS2017

Skillful Spring Forecasts of September Arctic Sea Ice Extent Using Passive Microwave Data

In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March-May, while the SIC forecasts produce the highest skill in June-August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

melt onset↗

NASA NTRS2016

Applications for ICESat-2 Data

NASA;s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, scheduled to launch no later than April 2018 (and currently slated for October 2017), is being developed to continue the multiyear observations of the earths surface elevation, ice, and clouds started by ICESat. To increase the use of the satellite data after launch, the ICESat-2 mission invested in an applications program aimed at innovatively applying the data in a variety of fields. The program provides a framework for building a broad and well-defined user community during the prelaunch period to maximize the use of data products after launch and to provide early insight into the range of potential uses of the mission data. Ideas and research on how altimetry data will be used for decision making arise from the end users; there-fore, the ICESat-2 mission is extending itself through its applications program.

Brown, Molly E.↗

NASA NTRS2015

SIMPL/AVIRIS-NG Greenland 2015: Flight Report

In August 2015, NASA conducted a two-aircraft, coordinated campaign based out of Thule Air Base, Greenland, in support of Ice, Cloud, and land Elevation Satellite‐2 (ICESat-2) algorithm development. The survey targeted the Greenland Ice Sheet and sea ice in the Arctic Ocean during the summer melt season. The survey was conducted with a photon-counting laser altimeter in one aircraft and an imaging spectrometer in the second aircraft. Ultimately, the mission, SIMPL/AVIRIS-NG Greenland 2015, conducted nine coordinated science flights, for a total of 37 flight hours over the ice sheet and sea ice.

Ice Sheet↗

NASA NTRS2014

Determination of Local Slope on the Greenland Ice Sheet Using a Multibeam Photon-Counting Lidar in Preparation for the ICESat-2 Mission

The greatest changes in elevation in Greenland and Antarctica are happening along the margins of the ice sheets where the surface frequently has significant slopes. For this reason, the upcoming Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission utilizes pairs of laser altimeter beams that are perpendicular to the flight direction in order to extract slope information in addition to elevation. The Multiple Altimeter Beam Experimental Lidar (MABEL) is a high-altitude airborne laser altimeter designed as a simulator for ICESat-2. The MABEL design uses multiple beams at fixed angles and allows for local slope determination. Here, we present local slopes as determined by MABEL and compare them to those determined by the Airborne Topographic Mapper (ATM) over the same flight lines in Greenland. We make these comparisons with consideration for the planned ICESat-2 beam geometry. Results indicate that the mean slope residuals between MABEL and ATM remain small (< 0.05◦) through a wide range of localized slopes using ICESat-2 beam geometry. Furthermore, when MABEL data are subsampled by a factor of 4 to mimic the planned ICESat-2 transmit-energy configuration, the results are indistinguishable from the full-data-rate analysis. Results from MABEL suggest that ICESat-2 beam geometry and transmit-energy configuration are appropriate for the determination of slope on ∼90-m spatial scales, a measurement that will be fundamental to deconvolving the effects of surface slope from the ice-sheet surface change derived from ICESat-2.

Greenland↗

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

Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation for the ICESat-2 Mission

NASA's Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission is a decadal survey mission (2016 launch). The mission objectives are to measure land ice elevation, sea ice freeboard, and changes in these variables, as well as to collect measurements over vegetation to facilitate canopy height determination. Two innovative components will characterize the ICESat-2 lidar: 1) collection of elevation data by a multibeam system and 2) application of micropulse lidar (photon-counting) technology. A photon-counting altimeter yields clouds of discrete points, resulting from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of the returned points to reflectors of interest. The objective of this paper is to derive an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2 data, based on airborne observations with a Sigma Space micropulse lidar. The mathematical algorithm uses spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors, and geostatistical classification parameters and hyperparameters. Validation shows that ground and canopy elevation, and hence canopy height, can be expected to be observable with high accuracy by ICESat-2 for all expected beam energies considered for instrument design (93.01%-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp), and 72.85%-98.68% for 0.48 msp). The algorithm derived here is generally applicable for elevation determination from photoncounting lidar altimeter data collected over forested areas, land ice, sea ice, and land surfaces, as well as for cloud detection.

Algorithms↗

NASA NTRS2013

An Ultra-Wideband, Microwave Radar for Measuring Snow Thickness on Sea Ice and Mapping Near-Surface Internal Layers in Polar Firn

Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100mW signal is swept from 2 to 8GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 c range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.

Snow Cover↗

NASA NTRS2013

Determination of Local Slope on the Greenland Ice Sheet Using a Multibeam Photon-Counting Lidar in Preparation for the ICESat-2 Mission

The greatest changes in elevation in Greenland and Antarctica are happening along the margins of the ice sheets where the surface frequently has significant slopes. For this reason, the upcoming Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission utilizes pairs of laser altimeter beams that are perpendicular to the flight direction in order to extract slope information in addition to elevation. The Multiple Altimeter Beam Experimental Lidar (MABEL) is a high-altitude airborne laser altimeter designed as a simulator for ICESat-2. The MABEL design uses multiple beams at fixed angles and allows for local slope determination. Here, we present local slopes as determined by MABEL and compare them to those determined by the Airborne Topographic Mapper (ATM) over the same flight lines in Greenland. We make these comparisons with consideration for the planned ICESat-2 beam geometry. Results indicate that the mean slope residuals between MABEL and ATM remain small (< 0.05 degrees) through a wide range of localized slopes using ICESat-2 beam geometry. Furthermore, when MABEL data are subsampled by a factor of 4 to mimic the planned ICESat-2 transmit-energy configuration, the results are indistinguishable from the full-data-rate analysis. Results from MABEL suggest that ICESat-2 beam geometry and transmit-energy configuration are appropriate for the determination of slope on approx. 90-m spatial scales, a measurement that will be fundamental to deconvolving the effects of surface slope from the ice-sheet surface change derived from ICESat-2.

Photon-Counting↗

NASA NTRS2012

First Assessments of Predicted ICESat-2 Performance Using Aircraft Data

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is a next-generation laser altimeter designed to continue key observations of ice sheet elevation change, sea ice freeboard, vegetation canopy height, earth surface elevation, and sea surface height. Scheduled for launch in mid-2016, ICESat-2 will use a high repetition rate (10 kHz), small footprint (10 m nominal ground diameter) laser, and a single-photon-sensitive detection strategy (photon counting) to measure precise range to the earth's surface. Using green light (532 nm), the six beams of ICESat-2 will provide improved spatial coverage compared with the single beam of ICESat, while the differences in transmit energy among the beams provide a large dynamic range. The six beams are arranged into three pairs of beams which allow slopes to measured on an orbit-by-orbit basis. In order to evaluate models of predicted ICESat-2 performance and provide ICESat-2-like data for algorithm development, an airborne ICESat-2 simulator was developed and first flown in 2010. This simulator, the Multiple Altimeter Beam Experimental Lidar (MABEL) was most recently deployed to Iceland in April 2012 and collected approx 85 hours of science data over land ice, sea ice, and calibration targets. MABEL uses a similar photon-counting measurement strategy to what will be used on ICESat-2. MABEL collects data in 16 green channels and an additional 8 channels in the infrared aligned across the direction of flight. By using NASA's ER-2 aircraft flying at 20km altitude, MABEL flies as close to space as is practical, and collects data through approx 95% of the atmosphere. We present background on the MABEL instrument, and data from the April 2012 deployment to Iceland. Among the 13 MABEL flights, we collected data over the Greenland ice sheet interior and outlet glaciers in the southwest and western Greenland, sea ice data over the Nares Strait and Greenland Sea, and a number of small glaciers and ice caps in Iceland and Svalbard. Several of the flights were coincident in time and space with NASA's Operation IceBridge, which provides an independent data set for validation. MABEL also collected data along CryoSat track 10482 in north central Greenland approximately one month after CryoSat passed overhead.

Neumann, Thomas↗

NASA NTRS2012

Satellite Observations of Antarctic Sea Ice Thickness and Volume

We utilize satellite laser altimetry data from ICESat combined with passive microwave measurements to analyze basin-wide changes in Antarctic sea ice thickness and volume over a 5 year period from 2003-2008. Sea ice thickness exhibits a small negative trend while area increases in the summer and fall balanced losses in thickness leading to small overall volume changes. Using a five year time-series, we show that only small ice thickness changes of less than -0.03 m/yr and volume changes of -266 cu km/yr and 160 cu km/yr occurred for the spring and summer periods, respectively. The calculated thickness and volume trends are small compared to the observational time period and interannual variability which masks the determination of long-term trend or cyclical variability in the sea ice cover. These results are in stark contrast to the much greater observed losses in Arctic sea ice volume and illustrate the different hemispheric changes of the polar sea ice covers in recent years.

Kurtz, Nathan↗

NASA NTRS2012

Moisture Fluxes Derived from EOS Aqua Satellite Data for the North Water Polynya Over 2003-2009

Satellite data were applied to calculate the moisture flux from the North Water polynya during a series of events spanning 2003-2009. The fluxes were calculated using bulk aerodynamic formulas with the stability effects according to the Monin-Obukhov similarity theory. Input parameters were taken from three sources: air relative humidity, air temperature, and surface temperature from the Atmospheric Infrared Sounder (AIRS) onboard NASA's Earth Observing System (EOS) Aqua satellite, sea ice concentration from the Advanced Microwave Scanning Radiometer (AMSR-E, also onboard Aqua), and wind speed from the ECMWF ERA-Interim reanalysis. Our results show the progression of the moisture fluxes from the polynya during each event, as well as their atmospheric effects after the polynya has closed up. These results were compared to results from studies on other polynyas, and fall within one standard deviation of the moisture flux estimates from these studies. Although the estimated moisture fluxes over the entire study region from AIRS are smaller in magnitude than ERA-Interim, they are more accurate due to improved temperature and relative humidity profiles and ice concentration estimates over the polynya. Error estimates were calculated to be 5.56 x10(exp -3) g/sq. m/ s, only 25% of the total moisture flux, thus suggesting that AIRS and AMSR-E can be used with confidence to study smaller scale features in the Arctic sea ice pack and can capture their atmospheric effects. These findings bode well for larger-scale studies of moisture fluxes over the entire Arctic Ocean and the thinning ice pack.

Boisvert, Linette N.↗