Validation activities for the Ice, Cloud, and Land Elevation Satellite - 2 (ICESat-2) Mission

2020 ◽  
Author(s):  
Thomas Neumann ◽  
Kelly Brunt ◽  
Lori Marguder ◽  
Nathan Kurtz
Keyword(s):  
Sea Ice ◽  
Global Scale ◽  
Green Laser ◽  
Airborne Lidar ◽  
Quality Data ◽  
Ice Cloud ◽  
Ongoing Work ◽  
Separate Channel ◽  
Photon Event ◽  
Individual Photon

<p>After launching on 15 September 2018, the Ice, Cloud, and Land Elevation Satellite – 2 (ICESat-2) Mission began collecting data on 14 October 2018.  The mission uses green laser light emitted by the Advanced Topographic Laser Altimetry System (ATLAS) to detect individual photons that are reflected by the Earth’s surface and returned to ATLAS.  These photons, when combined with information on the pointing direction, and position of the observatory in space, provide a geolocation and elevation for every measurement that spans the globe from 88 degrees north latitude to 88 degrees south.  The Global Geolocated Photon data product provides a latitude, longitude, elevation, and measurement time for each photon event telemetered to Earth for each of the instrument’s six beams. This product also delineates between high, medium, and low signal confidence levels and those measurements associated with background noise. The higher level, along-track products each use different strategies for photon aggregation to optimize the precision and accuracy of the surface retrievals over specific surface types. These types include land ice, sea ice, vegetation/land, ocean, and inland water. There is a separate channel dedicated to atmospheric returns to measure cloud and aerosols over a vertical window of 15 km. Calibration efforts utilized well designed on-orbit maneuvers to identify both pointing and range biases attributed to orbital variations on the satellite. Once corrected, the science-quality data products were released to the public in May 2019.</p><p> </p><p>In this presentation, we will present our ongoing work to evaluate and validate the geolocation and elevation accuracy and precision of measurements provided by the ICESat-2 mission.  The approaches are diverse in both location and methodology to ensure that we have a comprehensive assessment of the ATLAS performance variations throughout the orbital cycles. These strategies include comparisons with ground-based and airborne elevation measurements over the ice sheets, detailed analysis of returns from well-surveyed corner cube retro-reflectors, comparison of sea ice freeboard measured by airborne lidars, evaluation of global-scale ocean elevation through comparison with radar altimeters, and comparison of vegetation canopy height metrics measured by airborne lidar.  Our work to date demonstrates that individual photon elevations are accurate to approximately 30 cm vertically, and 6 m radially.  Aggregating many photons together reduces the elevation uncertainty to less than 5 cm for relatively flat and smooth ice sheet interiors.</p>

scholarly journals Canopy and Terrain Height Retrievals with ICESat-2: A First Look

2019 ◽  
Vol 11 (14) ◽  
pp. 1721 ◽  
Author(s):  
Amy L. Neuenschwander ◽  
Lori A. Magruder
Keyword(s):  
Sea Ice ◽  
Quantitative Assessment ◽  
Vantage Point ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Scientific Objective ◽  
Ice Cloud ◽  
Elevation Data ◽  
Airborne Lidar Data ◽  
Along Track

NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched in fall 2018 and has since collected continuous elevation data over the Earth’s surface. The primary scientific objective is to measure the cryosphere for studies related to land ice and sea ice characteristics. The vantage point from space, however, provides the opportunity to measure global surfaces including oceans, land, and vegetation. The ICESat-2 mission has dedicated products to the represented surface types, including an along-track elevation profile of terrain and canopy heights (ATL08). This study presents the first look at the ATL08 product and the quantitative assessment of the canopy and terrain height retrievals as compared to airborne lidar data. The study also provides qualitative examples of ICESat-2 observations from selected ecosystems to highlight the broad capability of the satellite for vegetation applications. Analysis of the mission’s preliminary ATL08 data product accuracy using an ICESat-2 transect over a vegetated region of Finland indicates a 5 m offset in geolocation knowledge (horizontal accuracy) well within the 6.5 m mission requirement. The vertical RMSE for the terrain and canopy height retrievals for one transect are 0.85 m and 3.2 m respectively.

How Well Can We Correct Systematic Errors in Historical XBT Data?

2018 ◽  
Vol 35 (5) ◽  
pp. 1103-1125 ◽  
Author(s):  
Lijing Cheng ◽  
Hao Luo ◽  
Timothy Boyer ◽  
Rebecca Cowley ◽  
John Abraham ◽  
...  
Keyword(s):  
Reference Data ◽  
World Ocean ◽  
Heat Content ◽  
Systematic Errors ◽  
Global Scale ◽  
Systematic Difference ◽  
Quality Data ◽  
High Quality Data ◽  
Sources Of Uncertainty ◽  
Global Datasets

AbstractBiases have been identified in historical expendable bathythermograph (XBT) datasets, which are one of the major sources of uncertainty in the ocean subsurface database. More than 10 correction schemes were proposed; however, their performance has not been collectively evaluated and compared. This study quantifies how well 10 different available schemes can correct the historical XBT data by comparing the corrected XBT data with collocated reference data in both the World Ocean Database (WOD) 2013 and the EN4 dataset. Four different metrics are proposed to quantify their performances. The results indicate CH14 is the best among the currently available methods, and L09/G12/GR10 can be used with some caveats. To test the robustness of the schemes, we further train the CH14 and L09 by using 50% of the XBT–reference data and the schemes are tested by using the remaining data. The results indicate that the two schemes are robust. Moreover, the EN4 and WOD comparison datasets show a systematic difference of XBT error (~0.01°C on a global scale and 0–700 m on average). influences of quality control and data processing have been investigated. Additionally, the side-by-side XBT–CTD comparison experiment is used to examine the correction schemes and provides independent high-quality data for the assessment. The schemes that best correct the global datasets do not always perform as well at correcting the side-by-side dataset, and further examination of the discrepancy in performance is still required. Finally, CH14 and L09 result in very similar ocean heat content (OHC) change estimates in the upper 700 m since 1966, suggesting the potential of reducing XBT-induced error in OHC estimates.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

scholarly journals Snow and ice applications of AVHRR in polar regions: report of a workshop held in Boulder, Colorado, 20 May 1992

1993 ◽  
Vol 17 ◽  
pp. 1-16 ◽  
Author(s):  
K. Steffen ◽  
R. Bindschadler ◽  
G. Casassa ◽  
J. Comiso ◽  
D. Eppler ◽  
...  
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Quality Data ◽  
Polar Regions ◽  
Data Set ◽  
Ice Surface ◽  
The Status ◽  
Ice Surface Temperature ◽  
Snow And Ice

The third symposium on Remote Sensing of Snow and Ice, organized by the International Glaciological Society, took place in Boulder, Colorado, 17–22 May 1992. As part of this meeting a total of 21 papers was presented on snow and ice applications of Advanced Very High Resolution Radiometer (AVHRR) satellite data in polar regions. Also during this meeting a NASA sponsored Workshop was held to review the status of polar surface measurements from AVHRR. In the following we have summarized the ideas and recommendations from the workshop, and the conclusions of relevant papers given during the regular symposium sessions. The seven topics discussed include cloud masking, ice surface temperature, narrow-band albedo, ice concentration, lead statistics, sea-ice motion and ice-sheet studies with specifics on applications, algorithms and accuracy, following recommendations for future improvements. In general, we can affirm the strong potential of AVHRR for studying sea ice and snow covered surfaces, and we highly recommend this satellite data set for long-term monitoring of polar process studies. However, progress is needed to reduce the uncertainty of the retrieved parameters for all of the above mentioned topics to make this data set useful for direct climate applications such as heat balance studies and others. Further, the acquisition and processing of polar AVHRR data must become better coordinated between receiving stations, data centers and funding agencies to guarantee a long-term commitment to the collection and distribution of high quality data.

scholarly journals Ice crystal number concentration estimates from lidar-radar satellite retrievals. Part 2: Controls on the ice crystal number concentration

2018 ◽  
Author(s):  
Edward Gryspeerdt ◽  
Odran Sourdeval ◽  
Johannes Quaas ◽  
Julien Delanoë ◽  
Philipp Kühne
Keyword(s):  
Global Scale ◽  
Number Concentration ◽  
Ice Crystal ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Radar Satellite ◽  
Mixed Phase Clouds

Abstract. The ice crystal number concentration (Ni) is a key property of ice clouds, both radiatively and microphysically. However, due to sparse in-situ measurements of ice cloud properties, the controls on the Ni have remained difficult to determine. As more advanced treatments of ice clouds are included in global models, it is becoming increasingly necessary to develop strong observational constraints on the processes involved. This work uses the DARDAR-LIM Ni retrieval described in part one to investigate the controls of the Ni at a global scale. The retrieved clouds are separated by type. The effects of temperature, proxies for in-cloud updraught and aerosol concentrations are investigated. Variations in the cloud top Ni (Ni(top)) consistent with both homogeneous and heterogeneous nucleation are observed and along with a possible role of aerosol both increasing and decreasing the Ni(top) depending on the prevailing meteorological situation. Away from the cloud top, the Ni displays a different sensitivity to these controlling factors, providing a possible explanation to the low Ni sensitivity to temperature and INP observed in previous in-situ studies. This satellite dataset provides a new way of investigating the response of cloud properties to meteorological and aerosol controls. The results presented in this work increase our confidence in the retrieved Ni and will form the basis for further study into the processes influencing ice and mixed phase clouds.

scholarly journals Airborne lidar measurements of surface ozone depletion over Arctic sea ice

2013 ◽  
Vol 13 (12) ◽  
pp. 6023-6029 ◽  
Author(s):  
J. A. Seabrook ◽  
J. A. Whiteway ◽  
L. H. Gray ◽  
R. Staebler ◽  
A. Herber
Keyword(s):  
Sea Ice ◽  
Ozone Concentration ◽  
Surface Ozone ◽  
Inverse Correlation ◽  
Arctic Sea Ice ◽  
Airborne Lidar ◽  
Back Trajectory ◽  
Surface Boundary ◽  
Arctic Sea ◽  
Lidar Measurements

Abstract. A differential absorption lidar (DIAL) for measurement of atmospheric ozone concentration was operated aboard the Polar 5 research aircraft in order to study the depletion of ozone over Arctic sea ice. The lidar measurements during a flight over the sea ice north of Barrow, Alaska, on 3 April 2011 found a surface boundary layer depletion of ozone over a range of 300 km. The photochemical destruction of surface level ozone was strongest at the most northern point of the flight, and steadily decreased towards land. All the observed ozone-depleted air throughout the flight occurred within 300 m of the sea ice surface. A back-trajectory analysis of the air measured throughout the flight indicated that the ozone-depleted air originated from over the ice. Air at the surface that was not depleted in ozone had originated from over land. An investigation into the altitude history of the ozone-depleted air suggests a strong inverse correlation between measured ozone concentration and the amount of time the air directly interacted with the sea ice.

scholarly journals Progress in satellite remote sensing for studying physical processes at the ocean surface and its borders with the atmosphere and sea ice

2016 ◽  
Vol 40 (2) ◽  
pp. 215-246 ◽  
Author(s):  
Jamie D. Shutler ◽  
Graham D. Quartly ◽  
Craig J. Donlon ◽  
Shubha Sathyendranath ◽  
Trevor Platt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Critical Review ◽  
Satellite Remote Sensing ◽  
Ocean Surface ◽  
Arctic Sea Ice ◽  
Heat Fluxes ◽  
Quality Data ◽  
Easy Access ◽  
Physical Oceanography

Physical oceanography is the study of physical conditions, processes and variables within the ocean, including temperature–salinity distributions, mixing of the water column, waves, tides, currents and air–sea interaction processes. Here we provide a critical review of how satellite sensors are being used to study physical oceanography processes at the ocean surface and its borders with the atmosphere and sea ice. The paper begins by describing the main sensor types that are used to observe the oceans (visible, thermal infrared and microwave) and the specific observations that each of these sensor types can provide. We then present a critical review of how these sensors and observations are being used to study: (i) ocean surface currents, (ii) storm surges, (iii) sea ice, (iv) atmosphere–ocean gas exchange and (v) surface heat fluxes via phytoplankton. Exciting advances include the use of multiple sensors in synergy to observe temporally varying Arctic sea ice volume, atmosphere–ocean gas fluxes, and the potential for four-dimensional water circulation observations. For each of these applications we explain their relevance to society, review recent advances and capability, and provide a forward look at future prospects and opportunities. We then more generally discuss future opportunities for oceanography-focused remote sensing, which includes the unique European Union Copernicus programme, the potential of the International Space Station and commercial miniature satellites. The increasing availability of global satellite remote-sensing observations means that we are now entering an exciting period for oceanography. The easy access to these high quality data and the continued development of novel platforms is likely to drive further advances in remote sensing of the ocean and atmospheric systems.

scholarly journals Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation

2014 ◽  
Vol 8 (4) ◽  
pp. 1607-1622 ◽  
Author(s):  
R. Ricker ◽  
S. Hendricks ◽  
V. Helm ◽  
H. Skourup ◽  
M. Davidson
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Global Scale ◽  
Significant Rise ◽  
Arctic Sea Ice ◽  
Synthetic Aperture ◽  
Radar Altimeter ◽  
Roughness Effects ◽  
Snow Layer ◽  
Ice Surface

Abstract. In the context of quantifying Arctic ice-volume decrease at global scale, the CryoSat-2 satellite was launched in 2010 and is equipped with the Ku band synthetic aperture radar altimeter SIRAL (Synthetic Aperture Interferometric Radar Altimeter), which we use to derive sea-ice freeboard defined as the height of the ice surface above the sea level. Accurate CryoSat-2 range measurements over open water and the ice surface of the order of centimetres are necessary to achieve the required accuracy of the freeboard-to-thickness conversion. Besides uncertainties of the actual sea-surface height and limited knowledge of ice and snow properties, the composition of radar backscatter and therefore the interpretation of radar echoes is crucial. This has consequences in the selection of retracker algorithms which are used to track the main scattering horizon and assign a range estimate to each CryoSat-2 measurement. In this study we apply a retracker algorithm with thresholds of 40, 50 and 80% of the first maximum of radar echo power, spanning the range of values used in the current literature. By using the selected retrackers and additionally results from airborne validation measurements, we evaluate the uncertainties of sea-ice freeboard and higher-level products that arise from the choice of the retracker threshold only, independent of the uncertainties related to snow and ice properties. Our study shows that the choice of retracker thresholds does have a significant impact on magnitudes of estimates of sea-ice freeboard and thickness, but that the spatial distributions of these parameters are less affected. Specifically we find mean radar freeboard values of 0.121 m (0.265 m) for the 40% threshold, 0.086 m (0.203 m) for the 50% threshold and 0.024 m (0.092 m) for the 80% threshold, considering first-year ice (multiyear ice) in March 2013. We show that the main source of freeboard and thickness uncertainty results from the choice of the retracker and the unknown penetration of the radar pulse into the snow layer in conjunction with surface roughness effects. These uncertainties can cause a freeboard bias of roughly 0.06–0.12 m. Furthermore we obtain a significant rise of 0.02–0.15 m of freeboard from March 2013 to November 2013 in the area for multiyear sea ice north of Greenland and Canada. Since this is unlikely, it gives rise to the assumption that applying different retracker thresholds depending on seasonal properties of the snow load is necessary in the future.

scholarly journals Analysis of a jet stream induced gravity wave associated with an observed ice cloud over Greenland

2004 ◽  
Vol 4 (5) ◽  
pp. 1183-1200 ◽  
Author(s):  
S. Buss ◽  
A. Hertzog ◽  
C. Hostettler ◽  
T. B. Bui ◽  
D. Lüthi ◽  
...  
Keyword(s):  
Gravity Wave ◽  
Airborne Lidar ◽  
Shear Instability ◽  
Horizontal Wind ◽  
Jet Stream ◽  
Ice Cloud ◽  
Wave Ray ◽  
Vertically Propagating ◽  
Wind Profiles ◽  
High Elevations

Abstract. A polar stratospheric ice cloud (PSC type II) was observed by airborne lidar above Greenland on 14 January 2000. It was the unique observation of an ice cloud over Greenland during the SOLVE/THESEO 2000 campaign. Mesoscale simulations with the hydrostatic HRM model are presented which, in contrast to global analyses, are capable to produce a vertically propagating gravity wave that induces the low temperatures at the level of the PSC afforded for the ice formation. The simulated minimum temperature is ~8 K below the driving analyses and ~4.5 K below the frost point, exactly coinciding with the location of the observed ice cloud. Despite the high elevations of the Greenland orography the simulated gravity wave is not a mountain wave. Analyses of the horizontal wind divergence, of the background wind profiles, of backward gravity wave ray-tracing trajectories, of HRM experiments with reduced Greenland topography and of several diagnostics near the tropopause level provide evidence that the wave is emitted from an intense, rapidly evolving, anticyclonically curved jet stream. The precise physical process responsible for the wave emission could not be identified definitely, but geostrophic adjustment and shear instability are likely candidates. In order to evaluate the potential frequency of such non-orographic polar stratospheric cloud events, the non-linear balance equation diagnostic is performed for the winter 1999/2000. It indicates that ice-PSCs are only occasionally generated by gravity waves emanating from spontaneous adjustment.

scholarly journals Effect of Turbidity, Temperature and Salinity of Waters on Depth Data from Airborne LiDAR Bathymetry

2021 ◽  
Vol 925 (1) ◽  
pp. 012056
Author(s):  
L R Saputra ◽  
I M Radjawane ◽  
H Park ◽  
H Gularso
Keyword(s):  
Water Column ◽  
Green Laser ◽  
Airborne Lidar ◽  
Water Parameters ◽  
Depth Data ◽  
Turbid Waters ◽  
Influence Of Water ◽  
Airborne Lidar Bathymetry ◽  
Index Value ◽  
Air Column

Abstract The influence of seawater parameters cannot be ignored when conducting bathymetric LiDAR (Laser Induced Detection and Ranging or Light Detection and Ranging) surveys such as turbidity, temperature, and salinity. Turbidity affects the attenuation diffusion coefficient of the green laser is penetrating the air column. The comparison of LiDAR bathymetric depth with Secchi disk depth is used as a reference in determining the effect of turbidity. The results are in locations with primarily clear water the ability of LiDAR can penetrate up to 7m, while in turbid waters up to 3m. On average, the ability of the green laser LiDAR bathymetry can penetrate the waters of 1.5-2 times the depth at the location of this study around the bay of Lampung Indonesia. Other water parameters are temperature and salinity. These parameters are used to calculate the refractive index value of water. The Different temperature and salinity values in a water column can result in differences in the accuracy of the bathymetry LiDAR depth of 4-6mm. The influence of water column parameters can be a concern in planning and processing airborne LiDAR altimetry (ALB) surveys.

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