Mei M. Jeffrey
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ArticleA textural approach to improving snow depth estimates in the Weddell Sea(MDPI, 2020-05-08) Mei, M. Jeffrey ; Maksym, TedThe snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2. Snow redistributed by wind collects around areas of deformed ice and forms a wide variety of features on sea ice; the morphology of these features may provide some indication of the mean snow depth. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow using snow surface freeboard measurements from Operation IceBridge campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, even when they have different absolute snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth with ∼22% error. We show that at the floe scale (∼180 m), snow depth can be directly estimated from the snow surface with ∼20% error using deep learning techniques, and that the learned filters are comparable to standard textural analysis techniques. This error drops to ∼14% when averaged over 1.5 km scales. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, as compared to current methods. Such methods may be useful for reducing uncertainty in Antarctic sea ice thickness estimates from ICESat-2.
ArticleCalving localization at Helheim Glacier using multiple local seismic stations(Copernicus Publications on behalf of the European Geosciences Union, 2017-02-22) Mei, M. Jeffrey ; Holland, David M. ; Anandakrishnan, Sridhar ; Zheng, TiantianA multiple-station technique for localizing glacier calving events is applied to Helheim Glacier in southeastern Greenland. The difference in seismic-wave arrival times between each pairing of four local seismometers is used to generate a locus of possible event origins in the shape of a hyperbola. The intersection of the hyperbolas provides an estimate of the calving location. This method is used as the P and S waves are not distinguishable due to the proximity of the local seismometers to the event and the emergent nature of calving signals. We find that the seismic waves that arrive at the seismometers are dominated by surface (Rayleigh) waves. The surface-wave velocity for Helheim Glacier is estimated using a grid search with 11 calving events identified at Helheim from August 2014 to August 2015. From this, a catalogue of 11 calving locations is generated, showing that calving preferentially happens at the northern end of Helheim Glacier.
ArticleEstimating early-winter Antarctic sea ice thickness from deformed ice morphology(European Geosciences Union, 2019-11-08) Mei, M. Jeffrey ; Maksym, Ted ; Weissling, Blake ; Singh, HanumantSatellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness (SIT) can be estimated from snow freeboard measurements, such as those from airborne/satellite lidar, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic SIT have errors as high as ∼50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate SIT at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate SIT at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ∼15 %). Moreover, the neural network does not require specification of snow depth or density, which increases its potential applications to other lidar datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season. This method may be extended to lower-resolution, larger-footprint data such as such as Operation IceBridge, and it suggests a possible avenue to reduce errors in satellite estimates of Antarctic SIT from ICESat-2 over current methods, especially at smaller scales.
ThesisMorphological approaches to understanding Antarctic Sea ice thickness(Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2020-09) Mei, M. JeffreySea ice thickness has long been an under-measured quantity, even in the satellite era. The snow surface elevation, which is far easier to measure, cannot be directly converted into sea ice thickness estimates without knowledge or assumption of what proportion of the snow surface consists of snow and ice. We do not fully understand how snow is distributed upon sea ice, in particular around areas with surface deformation. Here, we show that deep learning methods can be used to directly predict snow depth, as well as sea ice thickness, from measurements of surface topography obtained from laser altimetry. We also show that snow surfaces can be texturally distinguished, and that texturally-similar segments have similar snow depths. This can be used to predict snow depth at both local (sub-kilometer) and satellite (25 km) scales with much lower error and bias, and with greater ability to distinguish inter-annual and regional variability than current methods using linear regressions. We find that sea ice thickness can be estimated to ∼20% error at the kilometer scale. The success of deep learning methods to predict snow depth and sea ice thickness suggests that such methods may be also applied to temporally/spatially larger datasets like ICESat-2.