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1. Multidecadal Sea Level Prediction Using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data

   https://doi.org/10.1175/AIES-D-23-0089.1  

   Sinha, Saumya; Fasullo, John; Nerem, R Steven; Monteleoni, Claire (October 2024, Artificial Intelligence for the Earth Systems)

   Abstract Sea surface height observations provided by satellite altimetry since 1993 show a rising rate (3.4 mm yr⁻¹) for global mean sea level. While on average, sea level has risen 10 cm over the last 30 years, there is considerable regional variation in the sea level change. Through this work, we predict sea level trends 30 years into the future at a 2° spatial resolution and investigate the future patterns of the sea level change. We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting over the global ocean. Our approach incorporates sea level data from both altimeter observations and climate model simulations. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend based on climate model projections. Alongside this, our method provides uncertainty estimates associated with the ML prediction. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region. We compare two partitioning strategies: one achieved using domain knowledge and the other employing spectral clustering. Our results demonstrate that segmenting the spatial dataset with spectral clustering improves the ML predictions. Significance StatementLong-term projections are needed to help coastal communities adapt to sea level rise. Forecasting multidecadal sea level change is a complex problem. In this paper, we show the promise of machine learning in producing such forecasts 30 years in advance and over the global ocean. Continued improvements in prediction skills that build on this work will be vital in sea level rise adaptation efforts. 

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2. Water in Ice - It's Detection, Measurement and Role in Understanding Glacial Acceleration and Melt Processes from ICESat-2 Altimeter Data

   https://doi.org/10.22541/essoar.173524501.13997622/v1  

   Herzfeld, Ute Christina; Middleton, Rachel; Trantow, Thomas; Hayes, Adam N (December 2024, ESS Open Archive)
3. Spatiotemporal mapping of a large mountain glacier from CryoSat-2 altimeter data: surface elevation and elevation change of Bering Glacier during surge (2011–2014)

   https://doi.org/10.1080/01431161.2016.1187318  

   Trantow, T.; Herzfeld, U. C. (June 2016, International Journal of Remote Sensing)
4. Airborne Validation of ICESat-2 ATLAS Data over Crevassed Surfaces and Other Complex Glacial Environments: Results from Experiments of Laser Altimeter and Kinematic GPS Data Collection from a Helicopter over a Surging Arctic Glacier (Negribreen, Svalbard)

   https://doi.org/10.3390/rs14051185  

   Herzfeld, Ute C.; Lawson, Matthew; Trantow, Thomas; Nylen, Thomas (March 2022, Remote Sensing)

   The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7 m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905 nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1) Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2) Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3) Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25 m, while distance between predicted and actual ICESat-2 ground track was on the order of 9 m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4) The Lasertech Universal Laser System (ULS), operated at up to 300 m above ground level, yields full return frequency (400 Hz) and 0.06–0.08 m on-ice along-track spacing of height measurements. (5) Cross-over differences of airborne laser altimeter data are −0.172 ± 2.564 m along straight paths, which implies a precision of approximately 2.6 m for ICESat-2 validation experiments in crevassed terrain. (6) In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)–(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. Numerical evaluation of height comparisons utilizes spatial surface roughness measures. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge. 

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5. NSF COLDEX 2023-24 Riegl Laser Altimeter Level 2 Geolocated Surface Elevation Triplets:AlternativeTitle

   https://doi.org/10.18738/T8/PNBFOL  

   Young, Duncan; Ng, Gregory; Kerr, Megan E; Singh, Shivangini; Buhl, Dillon; Echeverry, Gonzalo; Richter, Thomas G; Kempf, Scott D; Blankenship, Donald D; Young, Duncan (January 2024, Texas Data Repository)

   These laser altimetry data were collected as part of the 2023-24 <a href="https://www.coldex.org">NSF COLDEX </a> CXA2 airborne campaign targeting the southern flank of East Antarctica's Dome A. In this Level 2 product, we have used the laser range to the surface and complementary aircraft position data to calculated the ice surface elevation, which is an important constraint on ice flow. Complementary radar, gravity, magnetics and imagery were also collected. <p> <i>Data format:</i>Data are formatted as text files with a header and the following tab delimited format columns. Data are in the same format as similar <a href="https://doi.org/10.5067/JV9DENETK13E"> IceBridge ILUTP2 altimetry data. </a> <p> Field 1: Year (UTC)<br> Field 2: Day of year (UTC)<br> Field 3: Second of day (UTC)<br> Field 4: Longitude Angle (deg) (WGS-84) <br> Field 5: Latitude Angle (deg) (WGS-84)<br> Field 6: Laser Derived Surface Elevation (m) (WGS-84)<br> <p> Missing values have been replaced by "nan". The effective footprint of the laser data is 25 m along track by 1 meter across track. Some cloud filtering was performed. <p> <i>Uncertainties</i>: A comparison of this laser altimetry dataset north of 87.5˚S with the <a href="https://doi.org/10.7910/DVN/EBW8UC"> REMAv2 </a>100 m mosaic digital terrain model indicate a median bias of 17 cm and a root mean squared (RMS) difference of 20 cm. Intersections between profiles within this survey, on the Antarctic Plateau but away from South Pole Station, have RMS differences of 6.8 cm. <p> <i>Datum: </i>WGS-84 ellipsoid; ITRF 2008 <p> <i>Geolocation: </i>Positioning and orientation for CXA1 came from loosely coupled joint PPP/inertial solutions using a Novatel OEM-4 GPS receiver and an iMAR FSAS IMU. <p> <i>Pointing bias: </i> roll: 0.340 degrees; pitch: -0.505 degrees <br> Pointing angle (pointing bias) is the angular offset of the downward-pointing laser boresight respect to the vehicle body frame's vertical (Z) axis. This estimated angle is derived by comparing measurements at crossovers. Pointing angle is provided in the vehicle body frame, using the laser origin for the rotation node. A positive pitch rotation indicates that the laser beam intersects the ground forward of the z-axis. A positive roll rotation indicates that the laser beam intersects the ground left of the z-axis. Pointing biases were found using the minimization of cross over difference method from <a href="http://dx.doi.org/10.3189/2015JoG14J048">Young et al., 2015</a>. <p> <i>Level arm: </i>X: 0 m; Y: 0.2 m; Z: -0.22 m <br> The lever arm is the position of the laser origin relative to the aircraft position solution, estimated using crossover-error minimization. Lever arm is provided in the vehicle body frame, with +X is forward, +Y is right, and +Z is down. Lever arm was measured after installation in the field. <p> <i>GNSS_antenna: </i>AeroAntenna AT1675-17W-TCNF-000-RG-36-NM <br> The coordinate system for the laser-gps lever arm is X forward, Y right, and Z down, from the center of position. 

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