Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix
The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many so...
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ftunivutrecht:oai:dspace.library.uu.nl:1874/433020 2023-12-10T09:53:55+01:00 Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix Chamberlain, Paul Talley, Lynne D. Mazloff, Matthew Van Sebille, Erik Gille, Sarah T. Tucker, Tyler Scanderbeg, Megan Robbins, Pelle Sub Physical Oceanography Marine and Atmospheric Research 2023-09-01 application/pdf https://dspace.library.uu.nl/handle/1874/433020 en eng 0739-0572 https://dspace.library.uu.nl/handle/1874/433020 info:eu-repo/semantics/EmbargoedAccess Advection Buoy observations Lagrangian circulation/transport Large-scale motions Ocean Statistical forecasting Taverne Ocean Engineering Atmospheric Science Article 2023 ftunivutrecht 2023-11-15T23:22:11Z The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 28° 3 28° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE). Article in Journal/Newspaper Southern Ocean Utrecht University Repository Southern Ocean |
institution |
Open Polar |
collection |
Utrecht University Repository |
op_collection_id |
ftunivutrecht |
language |
English |
topic |
Advection Buoy observations Lagrangian circulation/transport Large-scale motions Ocean Statistical forecasting Taverne Ocean Engineering Atmospheric Science |
spellingShingle |
Advection Buoy observations Lagrangian circulation/transport Large-scale motions Ocean Statistical forecasting Taverne Ocean Engineering Atmospheric Science Chamberlain, Paul Talley, Lynne D. Mazloff, Matthew Van Sebille, Erik Gille, Sarah T. Tucker, Tyler Scanderbeg, Megan Robbins, Pelle Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix |
topic_facet |
Advection Buoy observations Lagrangian circulation/transport Large-scale motions Ocean Statistical forecasting Taverne Ocean Engineering Atmospheric Science |
description |
The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 28° 3 28° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE). |
author2 |
Sub Physical Oceanography Marine and Atmospheric Research |
format |
Article in Journal/Newspaper |
author |
Chamberlain, Paul Talley, Lynne D. Mazloff, Matthew Van Sebille, Erik Gille, Sarah T. Tucker, Tyler Scanderbeg, Megan Robbins, Pelle |
author_facet |
Chamberlain, Paul Talley, Lynne D. Mazloff, Matthew Van Sebille, Erik Gille, Sarah T. Tucker, Tyler Scanderbeg, Megan Robbins, Pelle |
author_sort |
Chamberlain, Paul |
title |
Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix |
title_short |
Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix |
title_full |
Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix |
title_fullStr |
Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix |
title_full_unstemmed |
Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix |
title_sort |
using existing argo trajectories to statistically predict future float positions with a transition matrix |
publishDate |
2023 |
url |
https://dspace.library.uu.nl/handle/1874/433020 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
0739-0572 https://dspace.library.uu.nl/handle/1874/433020 |
op_rights |
info:eu-repo/semantics/EmbargoedAccess |
_version_ |
1784901093990334464 |