Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment
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Published:2023-08-17
Issue:8
Volume:17
Page:3329-3342
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ISSN:1994-0424
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Container-title:The Cryosphere
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language:en
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Short-container-title:The Cryosphere
Author:
Alonso-González EstebanORCID, Gascoin SimonORCID, Arioli SaraORCID, Picard GhislainORCID
Abstract
Abstract. The assimilation of data from Earth observation satellites into
numerical models is considered to be the path forward to estimate snow cover
distribution in mountain catchments, providing accurate information on the
mountainous snow water equivalent (SWE). The land surface temperature (LST)
can be observed from space, but its potential to improve SWE simulations
remains underexplored. This is likely due to the insufficient temporal or
spatial resolution offered by the current thermal infrared (TIR) missions.
However, three planned missions will provide global-scale TIR data at much
higher spatiotemporal resolution in the coming years. To investigate the value of TIR data to improve SWE estimation, we developed
a synthetic data assimilation (DA) experiment at five snow-dominated sites
covering a latitudinal gradient in the Northern Hemisphere. We generated
synthetic true LST and SWE series by forcing an energy balance snowpack
model with the ERA5-Land reanalysis. We used this synthetic true LST to
recover the synthetic true SWE from a degraded version of ERA5-Land. We
defined different observation scenarios to emulate the revisiting times of
Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for
High-resolution Natural resource Assessment (TRISHNA) (3 d) while
accounting for cloud cover. We replicated the experiments 100 times at each
experimental site to assess the robustness of the assimilation process with
respect to cloud cover under both revisiting scenarios. We performed the
assimilation using two different approaches: a sequential scheme (particle
filter) and a smoother (particle batch smoother). The results show that LST DA using the smoother reduced the normalized root
mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to
17 % and 13 % for 16 d revisit and 3 d revisit respectively in the
absence of clouds. We found similar but higher nRMSE values by removing
observations due to cloud cover but with a substantial increase in the
standard deviation of the nRMSE of the replicates, highlighting the
importance of revisiting times in the stability of the assimilation
performance. The smoother largely outperformed the particle filter
algorithm, suggesting that the capability of a smoother to propagate the
information along the season is key to exploit LST information for snow
modelling. Finally, we have compared the benefit of assimilating LST with
synthetic observations of fractional snow cover area (FSCA). LST DA
performed better than FSCA DA in all the study sites, suggesting that the
information provided by LST is not limited to the duration of the snow
season. These results suggest that the LST data assimilation has an
underappreciated potential to improve snowpack simulations and highlight the
value of upcoming TIR missions to advance snow hydrology.
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Water Science and Technology
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