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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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00062488 2023-05-15T18:32:33+02:00 Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles Odry, Jean Boucher, Marie-Amélie Lachance-Cloutier, Simon Turcotte, Richard St-Louis, Pierre-Yves 2022-09 electronic https://doi.org/10.5194/tc-16-3489-2022 https://noa.gwlb.de/receive/cop_mods_00062488 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061743/tc-16-3489-2022.pdf https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-3489-2022 https://noa.gwlb.de/receive/cop_mods_00062488 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061743/tc-16-3489-2022.pdf https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2022 ftnonlinearchiv https://doi.org/10.5194/tc-16-3489-2022 2022-09-04T23:11:54Z 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 Niedersächsisches Online-Archiv NOA Canada The Cryosphere 16 9 3489 3506 |
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article Verlagsveröffentlichung Odry, Jean Boucher, Marie-Amélie Lachance-Cloutier, Simon Turcotte, Richard St-Louis, Pierre-Yves Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles |
topic_facet |
article Verlagsveröffentlichung |
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 |
Odry, Jean Boucher, Marie-Amélie Lachance-Cloutier, Simon Turcotte, Richard St-Louis, Pierre-Yves |
author_facet |
Odry, Jean Boucher, Marie-Amélie Lachance-Cloutier, Simon Turcotte, Richard St-Louis, Pierre-Yves |
author_sort |
Odry, Jean |
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://noa.gwlb.de/receive/cop_mods_00062488 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061743/tc-16-3489-2022.pdf https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf |
geographic |
Canada |
geographic_facet |
Canada |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-3489-2022 https://noa.gwlb.de/receive/cop_mods_00062488 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061743/tc-16-3489-2022.pdf https://tc.copernicus.org/articles/16/3489/2022/tc-16-3489-2022.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
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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1766216733904863232 |