2026-08-14 –, Room 6
MemlsRetrieval.jl is a Julia reimplementation of the Microwave Emission Model of Layered Snowpacks (MEMLS). It leverages Julia’s type system and generic input types to evaluate typical forward simulations in microseconds with zero allocations while remaining fully differentiable. MemlsRetrieval.jl has been successfully combined with other models in an optimal estimation framework for the retrieval of geophysical parameters from satellite observations.
The Microwave Emission Model of Layered Snowpacks (MEMLS) is a widely used forward model for microwave emission from snow and sea ice, here we present a reimplementation in Julia as MemlsRetrieval.jl. While the original MATLAB package has been valuable for exploring the parameter space and comparisons to satellite observations, it was not designed for efficient inversion or large-scale application like satellite retrievals. Our Julia implementation shifts the focus toward performance, composability, and retrieval workflows.
By leveraging Julia’s type system together with packages such as StaticArrays.jl and ForwardDiff.jl, MemlsRetrieval.jl supports fast forward-model evaluation and automatic differentiation for Jacobian computation. In typical use cases of up to 10 layers, both forward evaluations and Jacobian calculations run in the microsecond range with zero allocations on consumer hardware. This enables efficient parallel execution with small memory footprints which is especially useful for retrieval and data-assimilation applications.
As a first application, we replaced a simplified sea-ice surface-emission parameterization in an ocean–sea-ice–atmosphere microwave emission model with MemlsRetrieval.jl. We then used this model within an optimal-estimation framework to retrieve multiple geophysical parameters, simultaneously from Advanced Microwave Scanning Radiometer 2 (AMSR2) satellite observations over the Arctic. The inclusion of our physical forward model allows us to exploit sensitivities that are often neglected because suitable empirical parameterizations are unavailable in practical retrieval systems. At the poster, we will present the package design, performance characteristics, and first retrieval results, and discuss how the model can support future cryospheric remote-sensing applications in Julia.
Marcus Huntemann is a researcher at the University of Bremen in the Institute of Environmental Physics. He works with Julia since since 2015 in various applications mainly involving geophysical modeling, image processing and Geophysical retrievals from satellite observations.