<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://www.cambridge.org/core/books/time-series-data-analysis-in-oceanography/9432681C2CFF7A0443658C8168E1C343?pageNum=2&amp;searchWithinIds=9432681C2CFF7A0443658C8168E1C343&amp;productType=BOOK_PART&amp;searchWithinIds=9432681C2CFF7A0443658C8168E1C343&amp;productType=BOOK_PART&amp;sort=mtdMetadata.bookPartMeta._mtdPositionSortable%3Aasc&amp;pageSize=30&amp;template=cambridge-core%2Fbook%2Fcontents%2Flistings&amp;ignoreExclusions=true#fndtn-information"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1910.04843"/>
	<rdf:li rdf:resource="https://www.bbc.com/future/article/20201119-atlantic-ocean-the-largest-seaweed-bloom-in-history"/>
	<rdf:li rdf:resource="https://www.annualreviews.org/doi/full/10.1146/annurev-environ-102017-025826"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.2923"/>
	<rdf:li rdf:resource="http://io9.com/5883622/meet-the-bloop-the-mysterious-sound-from-the-bottom-of-the-pacific-ocean"/>
	<rdf:li rdf:resource="http://www.press.uchicago.edu/books/detail.html?bookId=bo11461535"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="https://www.cambridge.org/core/books/time-series-data-analysis-in-oceanography/9432681C2CFF7A0443658C8168E1C343?pageNum=2&amp;searchWithinIds=9432681C2CFF7A0443658C8168E1C343&amp;productType=BOOK_PART&amp;searchWithinIds=9432681C2CFF7A0443658C8168E1C343&amp;productType=BOOK_PART&amp;sort=mtdMetadata.bookPartMeta._mtdPositionSortable%3Aasc&amp;pageSize=30&amp;template=cambridge-core%2Fbook%2Fcontents%2Flistings&amp;ignoreExclusions=true#fndtn-information">
    <title>Time Series Data Analysis in Oceanography</title>
    <dc:date>2022-06-30T18:03:40+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/time-series-data-analysis-in-oceanography/9432681C2CFF7A0443658C8168E1C343?pageNum=2&amp;searchWithinIds=9432681C2CFF7A0443658C8168E1C343&amp;productType=BOOK_PART&amp;searchWithinIds=9432681C2CFF7A0443658C8168E1C343&amp;productType=BOOK_PART&amp;sort=mtdMetadata.bookPartMeta._mtdPositionSortable%3Aasc&amp;pageSize=30&amp;template=cambridge-core%2Fbook%2Fcontents%2Flistings&amp;ignoreExclusions=true#fndtn-information</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Chunyan Li is a course instructor with many years of experience in teaching about time series analysis. His book is essential for students and researchers in oceanography and other subjects in the Earth sciences, looking for a complete coverage of the theory and practice of time series data analysis using MATLAB. This textbook covers the topic's core theory in depth, and provides numerous instructional examples, many drawn directly from the author's own teaching experience, using data files, examples, and exercises. The book explores many concepts, including time; distance on Earth; wind, current, and wave data formats; finding a subset of ship-based data along planned or random transects; error propagation; Taylor series expansion for error estimates; the least squares method; base functions and linear independence of base functions; tidal harmonic analysis; Fourier series and the generalized Fourier transform; filtering techniques: sampling theorems: finite sampling effects; wavelet analysis; and EOF analysis."

--- Last tag means: translate examples!]]></description>
<dc:subject>to:NB books:noted time_series oceanography to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:82a5be4ed244/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:oceanography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.04843">
    <title>[1910.04843] Late 19th-Century Navigational Uncertainties and Their Influence on Sea Surface Temperature Estimates</title>
    <dc:date>2020-12-15T13:05:40+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.04843</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Accurate estimates of historical changes in sea surface temperatures (SSTs) and their uncertainties are important for documenting and understanding historical changes in climate. A source of uncertainty that has not previously been quantified in historical SST estimates stems from position errors. A Bayesian inference framework is proposed for quantifying errors in reported positions and their implications on SST estimates. The analysis framework is applied to data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS3.0) in 1885, a time when astronomical and chronometer estimation of position was common, but predating the use of radio signals. Focus is upon a subset of 943 ship tracks from ICOADS3.0 that report their position every two hours to a precision of 0.01° longitude and latitude. These data are interpreted as positions determined by dead reckoning that are periodically updated by celestial correction techniques. The posterior medians of uncertainties in celestial correction are 33.1 km (0.30° on the equator) in longitude and 24.4 km (0.22°) in latitude, respectively. The posterior medians of two-hourly dead reckoning uncertainties are 19.2% for ship speed and 13.2° for ship heading, leading to random position uncertainties with median 0.18° (20 km on the equator) in longitude and 0.15° (17 km) in latitude. Reported ship tracks also contain systematic position uncertainties relating to precursor dead-reckoning positions not being updated after obtaining celestial position estimates, indicating that more accurate positions can be provided for SST observations. Finally, we translate position errors into SST uncertainties by sampling an ensemble of SSTs from the Multi-scale Ultra-high resolution Sea Surface Temperature (MURSST) data set."

--- I had not expected that reading all the Aubrey-Maturin books n times would help me understand a statistical climatology paper, but here we are.]]></description>
<dc:subject>to:NB climatology oceanography navigation measurement statistics data_collection pillai.natesh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b76c218e272f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climatology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:oceanography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:navigation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_collection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pillai.natesh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.bbc.com/future/article/20201119-atlantic-ocean-the-largest-seaweed-bloom-in-history">
    <title>The seaweed swamping the Atlantic Ocean - BBC Future</title>
    <dc:date>2020-11-29T18:52:00+00:00</dc:date>
    <link>https://www.bbc.com/future/article/20201119-atlantic-ocean-the-largest-seaweed-bloom-in-history</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[The role of fertilizer here is interesting.  It clearly indicates a waste of fertilizer, which isn't free --- why aren't farmers economizing on it?  Or on ways to make sure that more of what's applied is taken up by their crops?  And (putting on my "boy raised by <strike>wolves</strike> economist" hat) is there any way of charging farmers for fertilizer run-off, to internalize the cost it imposes on everyone?]]></description>
<dc:subject>climate_change oceanography ecology agriculture environmental_management</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee98d4412d06/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:oceanography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agriculture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:environmental_management"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/full/10.1146/annurev-environ-102017-025826">
    <title>Mapping Sea-Level Change in Time, Space, and Probability | Annual Review of Environment and Resources</title>
    <dc:date>2019-05-26T17:49:57+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/full/10.1146/annurev-environ-102017-025826</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Future sea-level rise generates hazards for coastal populations, economies, infrastructure, and ecosystems around the world. The projection of future sea-level rise relies on an accurate understanding of the mechanisms driving its complex spatio-temporal evolution, which must be founded on an understanding of its history. We review the current methodologies and data sources used to reconstruct the history of sea-level change over geological (Pliocene, Last Interglacial, and Holocene) and instrumental (tide-gauge and satellite alimetry) eras, and the tools used to project the future spatial and temporal evolution of sea level. We summarize the understanding of the future evolution of sea level over the near (through 2050), medium (2100), and long (post-2100) terms. Using case studies from Singapore and New Jersey, we illustrate the ways in which current methodologies and data sources can constrain future projections, and how accurate projections can motivate the development of new sea-level research questions across relevant timescales."

(Last tag unusually tentative)
]]></description>
<dc:subject>to:NB climate_change prediction oceanography to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:845bd35cc686/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:oceanography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.2923">
    <title>[1312.2923] Lagrangian Time Series Models for Ocean Surface Drifter Trajectories</title>
    <dc:date>2013-12-11T20:40:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.2923</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper proposes stochastic models for the analysis of ocean surface trajectories obtained from freely-drifting satellite-tracked instruments. The proposed time series models are used to summarise large multivariate dependent datasets and infer important physical parameters of inertial oscillations and other ocean processes. Nonstationary time series methods are employed to account for the spatiotemporal variability of each trajectory. Because the datasets are large, we construct computationally efficient methods through the use of frequency-domain modelling and estimation, with the data expressed as complex-valued time series. We detail how practical issues related to sampling and model misspecification may be addressed using semi-parametric techniques for time series, and we demonstrate the effectiveness of our stochastic models through application to both real-world data and to numerical model output."]]></description>
<dc:subject>to:NB oceanography spatio-temporal_statistics fluid_mechanics statistics time_series</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:751a6bb68b37/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:oceanography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatio-temporal_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fluid_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://io9.com/5883622/meet-the-bloop-the-mysterious-sound-from-the-bottom-of-the-pacific-ocean">
    <title>Meet the Bloop, the mysterious sound from the bottom of the Pacific Ocean</title>
    <dc:date>2012-02-09T17:28:33+00:00</dc:date>
    <link>http://io9.com/5883622/meet-the-bloop-the-mysterious-sound-from-the-bottom-of-the-pacific-ocean</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I wonder what Stefan Helmreich could do with these as samples.]]></description>
<dc:subject>cthulhiana bloop oceanography</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bdbcbd4572b7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cthulhiana"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bloop"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:oceanography"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.press.uchicago.edu/books/detail.html?bookId=bo11461535">
    <title>All the Fish in the Sea: Maximum Sustainable Yield and the Failure of Fisheries Management, Finley</title>
    <dc:date>2011-10-07T19:15:15+00:00</dc:date>
    <link>http://www.press.uchicago.edu/books/detail.html?bookId=bo11461535</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[“The decline and collapse of world fisheries is repeatedly cited as exemplary of the ‘tragedy of the commons’—the dilemma whereby individuals, acting in their own rational, individual self-interest, destroy a common good. Using extensive primary sources, Carmel Finley shows that this view is incorrect, and that the decline of fisheries had little to do with the inadvertent adverse impacts of individual action, and everything to do with deliberate governmental and international policy. Since the end of World War II, the United States has consciously pursued a policy of encouraging more and more and more fishing, a policy that had little to do with the needs or interests of fishermen (much less fish) and everything to do with U.S. strategic and economic interests. Not surprisingly, fishermen and fish suffered the consequences. It was a tragedy, but not of the commons. It was a tragedy of attempted enclosure. This is a very important book, one that no environmentalist can afford to ignore.”—Naomi Oreskes, University of California, San Diego]]></description>
<dc:subject>books:noted history_of_science ecology environmental_management commons to:NB fish oceanography</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:98527c89895b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:environmental_management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:commons"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fish"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:oceanography"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>