Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles
Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model prior to the forecasting phase. The goal is to ensure that the forecasts...
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fttriple:oai:gotriple.eu:oai:doaj.org/article:7eb2a8c9934d4363bc55c764a0e43e95 2023-05-15T18:32:19+02:00 Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles J. Odry M.-A. Boucher S. Lachance-Cloutier R. Turcotte P.-Y. St-Louis 2022-09-01 https://doi.org/10.5194/tc-16-3489-2022 https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf https://doaj.org/article/7eb2a8c9934d4363bc55c764a0e43e95 en eng Copernicus Publications doi:10.5194/tc-16-3489-2022 1994-0416 1994-0424 https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf https://doaj.org/article/7eb2a8c9934d4363bc55c764a0e43e95 undefined The Cryosphere, Vol 16, Pp 3489-3506 (2022) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2022 fttriple https://doi.org/10.5194/tc-16-3489-2022 2023-01-22T19:25:45Z Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model prior to the forecasting phase. The goal is to ensure that the forecasts are initialized from state variables that are as representative of reality as possible, and also to estimate the uncertainty of the state variables. There are several data assimilation methods, and particle filters are increasingly popular because of their minimal assumptions. The baseline idea is to produce an ensemble of scenarios (i.e. the particles) using perturbations of the forcing variables and/or state variables of the model. The different particles are weighted using the observations when they become available. However, implementing a particle filter over a domain with large spatial dimensions remains challenging, as the number of required particles rises exponentially as the domain size increases. Such a situation is referred to as the “curse of dimensionality”, or a “dimensionality limit”. A common solution to overcome this curse is to localize the particle filter. This consists in dividing the large spatial domain into smaller portions, or “blocks”, and applying the particle filter separately for each block. This can solve the above-mentioned dimensionality problem because it reduces the spatial scale at which each particle filter must be applied. However, it can also cause spatial discontinuities when the blocks are reassembled to form the whole domain. This issue can become even more problematic when additional data are assimilated. The purpose of this study is to test the possibility of remedying the spatial discontinuities of the particles by locally reordering them. We implement a spatialized particle filter to estimate the snow water equivalent (SWE) over a large territory in eastern Canada by assimilating local SWE observations from manual snow surveys. We apply two reordering strategies based on ... Article in Journal/Newspaper The Cryosphere Unknown Canada The Cryosphere 16 9 3489 3506 |
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geo envir J. Odry M.-A. Boucher S. Lachance-Cloutier R. Turcotte P.-Y. St-Louis Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
topic_facet |
geo envir |
description |
Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model prior to the forecasting phase. The goal is to ensure that the forecasts are initialized from state variables that are as representative of reality as possible, and also to estimate the uncertainty of the state variables. There are several data assimilation methods, and particle filters are increasingly popular because of their minimal assumptions. The baseline idea is to produce an ensemble of scenarios (i.e. the particles) using perturbations of the forcing variables and/or state variables of the model. The different particles are weighted using the observations when they become available. However, implementing a particle filter over a domain with large spatial dimensions remains challenging, as the number of required particles rises exponentially as the domain size increases. Such a situation is referred to as the “curse of dimensionality”, or a “dimensionality limit”. A common solution to overcome this curse is to localize the particle filter. This consists in dividing the large spatial domain into smaller portions, or “blocks”, and applying the particle filter separately for each block. This can solve the above-mentioned dimensionality problem because it reduces the spatial scale at which each particle filter must be applied. However, it can also cause spatial discontinuities when the blocks are reassembled to form the whole domain. This issue can become even more problematic when additional data are assimilated. The purpose of this study is to test the possibility of remedying the spatial discontinuities of the particles by locally reordering them. We implement a spatialized particle filter to estimate the snow water equivalent (SWE) over a large territory in eastern Canada by assimilating local SWE observations from manual snow surveys. We apply two reordering strategies based on ... |
format |
Article in Journal/Newspaper |
author |
J. Odry M.-A. Boucher S. Lachance-Cloutier R. Turcotte P.-Y. St-Louis |
author_facet |
J. Odry M.-A. Boucher S. Lachance-Cloutier R. Turcotte P.-Y. St-Louis |
author_sort |
J. Odry |
title |
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
title_short |
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
title_full |
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
title_fullStr |
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
title_full_unstemmed |
Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
title_sort |
large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-16-3489-2022 https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf https://doaj.org/article/7eb2a8c9934d4363bc55c764a0e43e95 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 16, Pp 3489-3506 (2022) |
op_relation |
doi:10.5194/tc-16-3489-2022 1994-0416 1994-0424 https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf https://doaj.org/article/7eb2a8c9934d4363bc55c764a0e43e95 |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/tc-16-3489-2022 |
container_title |
The Cryosphere |
container_volume |
16 |
container_issue |
9 |
container_start_page |
3489 |
op_container_end_page |
3506 |
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