Dataset Information

Characterizing Global Ozonesonde Profile Variability from Surface to the UT/LS with a Clustering Technique and MERRA-2 Reanalysis.

ABSTRACT: Our previous studies employing the self-organizing map (SOM) clustering technique to ozonesonde data have found significant links among meteorological and chemical regimes, and the shape of the ozone (O3) profile from the troposphere to the lower stratosphere. These studies, which focused on specific northern hemisphere mid-latitude geographical regions, demonstrated the advantages of SOM clustering by quantifying O3 profile variability and the O3/meteorological correspondence. We expand SOM to a global set of ozonesonde profiles spanning 1980-present from 30 sites to summarize the connections among O3 profiles, meteorology, and chemistry, using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis and other ancillary data. Four clusters of O3 mixing ratio profiles from the surface to the upper troposphere/lower stratosphere (UT/LS) are generated for each site, which show dominant profile shapes and typical seasonality (or lack thereof) that generally correspond to latitude (i.e. Tropical, Subtropical, Mid-Latitude, Polar). Examination of MERRA-2 output reveals a clear relationship among SOM clusters and covarying meteorological fields (geopotential height, potential vorticity, and tropopause height) for Polar and Mid-latitude sites. However, these relationships break down within ±30° latitude. Carbon monoxide satellite data, along with velocity potential, a proxy for convection, calculated from MERRA-2 wind fields assist characterization of the Tropical and Subtropical sites, where biomass burning and convective transport linked to the Madden-Julian Oscillation (MJO) dominate O3 variability. In addition to geophysical characterization of O3 profile variability, these results can be used to evaluate chemical transport model output and satellite measurements of O3.

SUBMITTER: Stauffer RM

PROVIDER: S-EPMC7580826 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Characterizing Global Ozonesonde Profile Variability from Surface to the UT/LS with a Clustering Technique and MERRA-2 Reanalysis.

Stauffer Ryan M RM Thompson Anne M AM Witte Jacquelyn C JC

Journal of geophysical research. Atmospheres : JGR 20180504 11

Our previous studies employing the self-organizing map (SOM) clustering technique to ozonesonde data have found significant links among meteorological and chemical regimes, and the shape of the ozone (O3) profile from the troposphere to the lower stratosphere. These studies, which focused on specific northern hemisphere mid-latitude geographical regions, demonstrated the advantages of SOM clustering by quantifying O3 profile variability and the O3/meteorological ...[more]

PMID: 33101823

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Project description:BackgroundEnsemble attribute profile clustering is a novel, text-based strategy for analyzing a user-defined list of genes and/or proteins. The strategy exploits annotation data present in gene-centered corpora and utilizes ideas from statistical information retrieval to discover and characterize properties shared by subsets of the list. The practical utility of this method is demonstrated by employing it in a retrospective study of two non-overlapping sets of genes defined by a published investigation as markers for normal human breast luminal epithelial cells and myoepithelial cells.ResultsEach genetic locus was characterized using a finite set of biological properties and represented as a vector of features indicating attributes associated with the locus (a gene attribute profile). In this study, the vector space models for a pre-defined list of genes were constructed from the Gene Ontology (GO) terms and the Conserved Domain Database (CDD) protein domain terms assigned to the loci by the gene-centered corpus LocusLink. This data set of GO- and CDD-based gene attribute profiles, vectors of binary random variables, was used to estimate multiple finite mixture models and each ensuing model utilized to partition the profiles into clusters. The resultant partitionings were combined using a unanimous voting scheme to produce consensus clusters, sets of profiles that co-occurred consistently in the same cluster. Attributes that were important in defining the genes assigned to a consensus cluster were identified. The clusters and their attributes were inspected to ascertain the GO and CDD terms most associated with subsets of genes and in conjunction with external knowledge such as chromosomal location, used to gain functional insights into human breast biology. The 52 luminal epithelial cell markers and 89 myoepithelial cell markers are disjoint sets of genes. Ensemble attribute profile clustering-based analysis indicated that both lists contained groups of genes with the functional properties of membrane receptor biology/signal transduction and nucleic acid binding/transcription. A subset of the luminal markers was associated with metabolic and oxidoreductase activities, whereas a subset of myoepithelial markers was associated with protein hydrolase activity.ConclusionGiven a set of genes and/or proteins associated with a phenomenon, process or system of interest, ensemble attribute profile clustering provides a simple method for collating and sythesizing the annotation data pertaining to them that are present in text-based, gene-centered corpora. The results provide information about properties common and unique to subsets of the list and hence insights into the biology of the problem under investigation.

Dataset Information

Characterizing Global Ozonesonde Profile Variability from Surface to the UT/LS with a Clustering Technique and MERRA-2 Reanalysis.

Publications

Characterizing Global Ozonesonde Profile Variability from Surface to the UT/LS with a Clustering Technique and MERRA-2 Reanalysis.

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