Alamgir Hossan

Liquid water amount (LWA) in seasonal snow controls meltwater retention, refreezing, and runoff. The retrieval of LWA from L-band brightness temperature (TB) observations remains highly sensitive to the effective permittivity of wet snow. Classical wet snow microstructure studies indicate that low liquid water content (LWC) wet snow contains both interfacial/contact-scale liquid and thicker localized pore-space water, whereas existing mixing formulas assume a single liquid water morphology. In this paper, we introduce a Maxwell-based dual-morphology wet-snow model that partitions liquid water between thin-coated interfacial water and localized thicker Tri-continuous (Tri-C) water through a morphology parameter (α) defined as the fraction of the total liquid water assigned to the thin-coated component. A permittivity database generated from computer-generated microstructures and numerical Maxwell solutions is used to train a neural-network emulator for retrieval. For a representative case with LWC = 2 % and snow density 400 kg/m³, varying α from 0 to 1 increases the imaginary part of the effective permittivity by a factor of 37, demonstrating first-order morphology-control on L-band dielectric loss. We combine this model with a two-layer radiative transfer framework and a temporally constrained inversion of the vertically and horizontally polarized SMAP TB at six Greenland percolation-zone AWS sites (CP1, DY2, KAN_U, NSE, SDL, and SDM) to retrieve α, LWC, wet layer thickness, and LWA. The retrieval reproduces the observed morning-pass seasonal evolution and suggests relatively larger thin-coated contributions at melt onset, Tri-C dominance during peak melt, and an incomplete late-season return toward thin-coated dominance, likely due to incompletely refrozen earlier meltwater. These results show that explicit liquid-water morphology can substantially improve L-band retrieval of wet snow compared with traditional mixing models.

Liquid water amount (LWA) in seasonal snow controls meltwater retention, refreezing, and runoff. The retrieval of LWA from L-band brightness temperature (TB) observations remains highly sensitive to the effective permittivity of wet snow. Classical wet snow microstructure studies indicate that low liquid water content (LWC) wet snow contains both interfacial/contact-scale liquid and thicker localized pore-space water, whereas existing mixing formulas assume a single liquid water morphology. In this paper, we introduce a Maxwell-based dual-morphology wet-snow model that partitions liquid water between thin-coated interfacial water and localized thicker Tri-continuous (Tri-C) water through a morphology parameter (α) defined as the fraction of the total liquid water assigned to the thin-coated component. A permittivity database generated from computer-generated microstructures and numerical Maxwell solutions is used to train a neural-network emulator for retrieval. For a representative case with LWC = 2 % and snow density 400 kg/m³, varying α from 0 to 1 increases the imaginary part of the effective permittivity by a factor of 37, demonstrating first-order morphology-control on L-band dielectric loss. We combine this model with a two-layer radiative transfer framework and a temporally constrained inversion of the vertically and horizontally polarized SMAP TB at six Greenland percolation-zone AWS sites (CP1, DY2, KAN_U, NSE, SDL, and SDM) to retrieve α, LWC, wet layer thickness, and LWA. The retrieval reproduces the observed morning-pass seasonal evolution and suggests relatively larger thin-coated contributions at melt onset, Tri-C dominance during peak melt, and an incomplete late-season return toward thin-coated dominance, likely due to incompletely refrozen earlier meltwater. These results show that explicit liquid-water morphology can substantially improve L-band retrieval of wet snow compared with traditional mixing models.

Liquid water amount (LWA) in seasonal snow controls meltwater retention, refreezing, and runoff. The retrieval of LWA from L-band brightness temperature (TB) observations remains highly sensitive to the effective permittivity of wet snow. Classical wet snow microstructure studies indicate that low liquid water content (LWC) wet snow contains both interfacial/contact-scale liquid and thicker localized pore-space water, whereas existing mixing formulas assume a single liquid water morphology. In this paper, we introduce a Maxwell-based dual-morphology wet-snow model that partitions liquid water between thin-coated interfacial water and localized thicker Tri-continuous (Tri-C) water through a morphology parameter (α) defined as the fraction of the total liquid water assigned to the thin-coated component. A permittivity database generated from computer-generated microstructures and numerical Maxwell solutions is used to train a neural-network emulator for retrieval. For a representative case with LWC = 2 % and snow density 400 kg/m3, varying α from 0 to 1 increases the imaginary part of the effective permittivity by a factor of 37, demonstrating first-order morphology-control on L-band dielectric loss. We combine this model with a two-layer radiative transfer framework and a temporally constrained inversion of the vertically and horizontally polarized SMAP TB at six Greenland percolation-zone AWS sites (CP1, DY2, KAN_U, NSE, SDL, and SDM) to retrieve α, LWC, wet layer thickness, and LWA. The retrieval reproduces the observed morning-pass seasonal evolution and suggests relatively larger thin-coated contributions at melt onset, Tri-C dominance during peak melt, and an incomplete late-season return toward thin-coated dominance, likely due to incompletely refrozen earlier meltwater. These results show that explicit liquid-water morphology can substantially improve L-band retrieval of wet snow compared with traditional mixing models.

The Queen Maud Land (QML) sector of East Antarctica comprises a complex system of grounded ice sheet and fringing ice shelves that regulate ice discharge to the Southern Ocean. Ice-sheet evolution in this region is controlled by interactions between atmospheric forcing, katabatic winds, and bedrock topography, producing strong spatial variability in accumulation, flow, and thermal regimes. While the bordering ice shelves currently act as stabilising buttresses, they are sensitive to oceanic heat intrusions, changing sea-ice conditions, and episodic surface melt. Melt–refreeze processes enhance firn compaction, weaken surface integrity, and may precondition ice shelves for hydrofracture under future warming, despite QML presently exhibiting a positive mass balance trend.We investigate the thermal and structural evolution of snow, firn, and ice in QML using an integrated framework that combines multi-frequency passive microwave observations with physically based modelling. Passive microwave measurements provide complementary sensitivity to near-surface melt processes and deeper firn and ice structure, enabling the detection of both contemporary melt signals and long-term subsurface changes. Lower-frequency observations penetrate deep into the firn and ice column, whereas higher-frequency observations respond to surface temperature, liquid water content, and accumulation variability.Snow, firn, and ice evolution is simulated using the Glacier Energy and Mass Balance (GEMB) model, running on the Ice Sheet and Sea Level System Model (ISSM), providing vertical profiles of temperature, density, and liquid water content driven by meteorological forcing. These profiles are used to forward-model microwave brightness temperatures with the Microwave Emission Model of Layered Snowpacks (MEMLS) across frequencies from 1.4 to 36.5 GHz, accounting for densification and refrozen ice layers. Modelled brightness temperatures are evaluated against satellite observations, providing twice-daily coverage of QML since 2010.We present spatial and temporal patterns of grounded ice-sheet structure, surface and subsurface temperature variability, fresh snow accumulation, and ice shelf melt signatures, together with residuals between observed and modelled brightness temperatures. Our results demonstrate the value of radiometric modelling for constraining firn structure, melt processes, and ice-shelf vulnerability in regions with sparse in situ data. By integrating passive microwave observations with physical firn models, this work supports improved calibration, initialisation, and confidence in projections of mass balance and structural evolution in the Queen Maud Land sector of East Antarctica.

Surface melt strongly influences the thermodynamics and stability of Antarctic ice shelves, yet firn models remain poorly constrained due to scarce observations. We present a framework that integrates satellite-borne microwave radiometry with firn modelling to evaluate meltwater generation and infiltration. Using the Glacier Energy and Mass Balance (GEMB) surface energy balance model, we simulate snow and firn temperature, density, and liquid water content (LWC) on the Shackleton Ice Shelf. These outputs drive the Microwave Emission of Model of Layered Snowpacks (MEMLS) radiative transfer model to simulate microwave brightness temperatures (TB), which we compare with satellite radiometer observations at 1.4 and 36.5 GHz. Because TB is highly sensitive to LWC, this approach provides a powerful constraint on firn thermodynamics in data-sparse regions. Our results reveal that realistic simulation of meltwater percolation and refreezing requires substantially finer vertical resolution than typically employed. Incorporating radiometric constraints thus improves firn evolution modelling and advances projections of Antarctic ice shelf stability.

Surface melt strongly influences the thermodynamics and stability of Antarctic ice shelves, yet firn models remain poorly constrained due to scarce observations. We present a framework that integrates satellite-borne microwave radiometry with firn modelling to evaluate meltwater generation and infiltration. Using the Glacier Energy and Mass Balance (GEMB) surface energy balance model, we simulate snow and firn temperature, density, and liquid water content (LWC) on the Shackleton Ice Shelf. These outputs drive the Microwave Emission of Model of Layered Snowpacks (MEMLS) radiative transfer model to simulate microwave brightness temperatures (TB), which we compare with satellite radiometer observations at 1.4 and 36.5 GHz. Because TB is highly sensitive to LWC, this approach provides a powerful constraint on firn thermodynamics in data-sparse regions. Our results reveal that realistic simulation of meltwater percolation and refreezing requires substantially finer vertical resolution than typically employed. Incorporating radiometric constraints thus improves firn evolution modelling and advances projections of Antarctic ice shelf stability.

As melting of the firn layer across the Greenland Ice Sheet increases, determining the fate of infiltrating meltwater is crucial for assessing the evolution of the firn and its potential to buffer runoff. While L-band satellite radiometry can estimate the vertically integrated liquid water amount (LWA), using such retrievals in firn studies is challenging because resolving vertical LWA profiles requires complex inversions or physically tuned models. In this study, we developed and tested a decoder consisting of long short-term memory (LSTM) and convolutional neural network (CNN) layers that assigns a vertical profile to the liquid water measured by L-band radiometry. We trained the decoder on an ensemble of time series generated by the SLF-SNOWPACK model to generate a latent representation of the depth distribution of LWA as determined by model physics. The decoder reproduced the relationship between LWA time series and the modeled liquid mass per cell, maximum depth of infiltration, and duration of wetting with mean errors of 29%, 15%, and 6%, respectively. We then applied the decoder to the L-band retrieved LWA time series for a low and high-melt-intensity year and assessed time/space trends in the refreezing amount and maximum depth of infiltration. The patterns are consistent with expectations from observations, lending confidence to the predictions. The method thus utilizes observational constraints from L-band time series to create vertical profiles of water and refreezing in firn, which can be incorporated into firn models and serve as validation targets for regional climate models.

Surface melt strongly influences the thermodynamics and stability of Antarctic ice shelves, yet firn models remain poorly constrained due to scarce observations. We present a framework that integrates satellite-borne microwave radiometry with firn modelling to evaluate meltwater generation and infiltration. Using the Glacier Energy and Mass Balance (GEMB) surface energy balance model, we simulate snow and firn temperature, density, and liquid water content (LWC) on the Shackleton Ice Shelf. These outputs drive the Microwave Emission of Model of Layered Snowpacks (MEMLS) radiative transfer model to simulate microwave brightness temperatures (TB), which we compare with satellite radiometer observations at 1.4 and 36.5 GHz. Because TB is highly sensitive to LWC, this approach provides a powerful constraint on firn thermodynamics in data-sparse regions. Our results reveal that realistic simulation of meltwater percolation and refreezing requires substantially finer vertical resolution than typically employed. Incorporating radiometric constraints thus improves firn evolution modelling and advances projections of Antarctic ice shelf stability.

Surface melt strongly influences the thermodynamics and stability of Antarctic ice shelves, yet firn models remain poorly constrained due to scarce observations. We present a framework that integrates satellite-borne microwave radiometry with firn modelling to evaluate meltwater generation and infiltration. Using the Glacier Energy and Mass Balance (GEMB) surface energy balance model, we simulate snow and firn temperature, density, and liquid water content (LWC) on the Shackleton Ice Shelf. These outputs drive the Microwave Emission of Model of Layered Snowpacks (MEMLS) radiative transfer model to simulate microwave brightness temperatures (TB), which we compare with satellite radiometer observations at 1.4 and 36.5 GHz. Because TB is highly sensitive to LWC, this approach provides a powerful constraint on firn thermodynamics in data-sparse regions. Our results reveal that realistic simulation of meltwater percolation and refreezing requires substantially finer vertical resolution than typically employed. Incorporating radiometric constraints thus improves firn evolution modelling and advances projections of Antarctic ice shelf stability.