Preprints
https://doi.org/10.5194/egusphere-2023-1152
https://doi.org/10.5194/egusphere-2023-1152
16 Jun 2023
 | 16 Jun 2023

Subgridding High Resolution Numerical Weather Forecast in the Canadian Selkirk range for local snow modelling in a remote sensing perspective

Paul Billecocq, Alexandre Langlois, and Benoit Montpetit

Abstract. Snow Water Equivalent (SWE) is a key variable in climate and hydrology studies. Current SWE products mask out high topography areas due to the coarse resolution of the satellite sensors used. The snow remote sensing community is hence pushing towards active microwaves approaches for global SWE monitoring. However, designing a SWE retrieval algorithm is not trivial, as multiple combinations of snow microstructure representations and SWE can yield the same radar signal. The community is converging towards forward modeling approaches using an educated first guess on the snowpack structure. Yet, snow highly varies in space and time, especially in mountain environments where the complex topography affects atmospheric and snowpack state variables in numerous ways. Automatic Weather Stations (AWS) are too sparse, and high-resolution Numerical Weather Predictions systems have a maximal resolution of 2.5 km × 2.5 km, which is too coarse to capture snow spatial variability in a complex topography. In this study, we designed a subgridding framework for the Canadian High Resolution Deterministic Prediction System. The native 2.5 km × 2.5 km resolution forecast was subgridded to a 100 m × 100 m resolution and used as the input for snow modeling over two winters in Glacier National Park, British Columbia, Canada. Air temperature, relative humidity, precipitation and wind speed were first parameterized regarding elevation using six Automatic Weather Stations. Alpine3D was then used to spatialize atmospheric parameters and radiation input accounting for terrain reflections and perform the snow simulations. Modeled snowpack state variables relevant for microwave remote sensing were evaluated against profiles generated with Automatic Weather Stations data and compared to raw HRDPS driven profiles. Overall, the subgridding framework improves the optical grain size (OGS) bias by 0.04 mm, the density bias by 2.7 kg · m−3 and the modelled SWE by 17 % (up to 41 % in the best case scenario). Overall, this work provides the necessary basis for SWE retrieval algorithms using forward modeling in a Bayesian framework.

Paul Billecocq, Alexandre Langlois, and Benoit Montpetit

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1152', Anonymous Referee #1, 20 Aug 2023
    • AC2: 'Reply on RC1', Paul Billecocq, 28 Nov 2023
  • RC2: 'Comment on egusphere-2023-1152', Anonymous Referee #2, 30 Aug 2023
    • AC1: 'Reply on RC2', Paul Billecocq, 28 Nov 2023
Paul Billecocq, Alexandre Langlois, and Benoit Montpetit
Paul Billecocq, Alexandre Langlois, and Benoit Montpetit

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Short summary
Snow covers a vast part of the globe, making Snow Water Equivalent (SWE) crucial for climate science and hydrology. SWE can be measured by satellite, but the snow's complex structure highly affects the signal and thus an educated first guess is mandatory. In this study, a subgridding framework was developped to model snow at the local scale from model weather data. The framework enhanced both weather parameters and snow modeling, paving the way for SWE inversion algorithms from satellite data.