An Ensemble-Based Probabilistic Score Approach to Compare Observation Scenarios: An Application to Biogeochemical-Argo Deployments

A cross-validation algorithm is developed to perform probabilistic observing system simulation experiments (OSSEs). The use of a probability distribution of “true” states is considered rather than a single “truth” using a cross-validation algorithm in which each member of an ensemble simulation is a...

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Bibliographic Details
Published in:Journal of atmospheric and oceanic technology 2019-12, Vol.36 (12), p.2307-2326
Main Authors: Germineaud, Cyril, Brankart, Jean-Michel, Brasseur, Pierre
Format: Article
Language:English
Subjects:
Algorithms
Arrays
Biogeochemistry
Chlorophyll
Chlorophylls
Collaboration
Computer simulation
Data
Data assimilation
Drifters
Earth Sciences
Ecosystems
Environmental Engineering
Environmental Sciences
Error reduction
Evaluation
Experiments
Floats
Ocean color
Ocean colour
Oceanography
Oceans
Physics
Probability distribution
Probability theory
Satellite observation
Satellites
Sciences of the Universe
Sensors
Simulation
Spatial data
Statistical analysis
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Summary:A cross-validation algorithm is developed to perform probabilistic observing system simulation experiments (OSSEs). The use of a probability distribution of “true” states is considered rather than a single “truth” using a cross-validation algorithm in which each member of an ensemble simulation is alternatively used as the “truth” and to simulate synthetic observation data that reflect the observing system to be evaluated. The other available members are used to produce an updated ensemble by assimilating the specific data, while a probabilistic evaluation of the observation impacts is obtained using a comprehensive set of verification skill scores. To showcase this new type of OSSE studies with tractable numerical costs, a simple biogeochemical application under the Horizon 2020 AtlantOS project is presented for a single assimilation time step, in order to investigate the value of adding biogeochemical (BGC)-Argo floats to the existing satellite ocean color observations. Further experiments must be performed in time as well for a rigorous and effective evaluation of the BGC-Argo network design, though some evidence from this preliminary work suggests that assimilating chlorophyll data from a BGC-Argo array of 1000 floats can provide additional error reduction at the surface, where the use of spatial ocean color data is limited (due to cloudy conditions), as well at depths ranging from 50 to 150 m.
ISSN:0739-0572
1520-0426
DOI:10.1175/JTECH-D-19-0002.1