Networks World History Book

[Nikita Desjardins]

Amap illustrating the interconnectivity and extent of the global trade networks at the onset of the 13th century after the centuries of decline and isolation following the collapse of the Western Roman Empire. The conditions were beginning to change, and trade between Christian and Muslim realms expanded (although still marred by a state of continuous hostilities), turning the Mediterranean once more into a bridge between the European West and the lands of North Africa and the Middle East. As Europe recovered from the destruction and mayhem of invasions and war, it had goods to sell - the export of cloth and metalworks could pay for imported silks, spices, and exotic goods from the Muslim world, Byzantine Empire, China, India, and beyond.

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The Global Historical Climatology Network daily (GHCNd) is an integrated database of daily climate summaries from land surface stations across the globe. GHCNd is made up of daily climate records from numerous sources that have been integrated and subjected to a common suite of quality assurance reviews.

GHCNd contains records from more than 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth. About half the stations only report precipitation. Both record length and period of record vary by station and cover intervals ranging from less than a year to more than 175 years.

Daily data from GHCN are available as individual ASCII (i.e., plain text) files (one file per station) and as a GZIP-compressed TAR file containing all of the station files. Please read the GHCNd readme file for details on the files available and for a description of the data format.

GHCNd receives daily updates from a variety of data streams, which also go through a suite of quality checks. Additionally, the dataset is reconstructed each weekend from more than 25 data source components to ensure that GHCNd is generally in sync with its growing list of constituent sources. During this process, the system applies quality assurance checks to the full dataset.

Each daily update to GHCNd is assigned a unique version number, and then archived at NCEI. GHCNd also serves as the official archive for the U.S. Cooperative Observer data, which have been comprehensively integrated into the dataset with other U.S. daily data sources.

Users are encouraged to see further notes on the Source Data section below regarding real-time versus time-delayed, archive quality updates. Generally, real-time replacement of updated data streams are replaced by archive-ready data sources 45 to 60 days after the end of a month.

Major changes to the processing system, as well as announcements of significant data additions to GHCNd are provided via an RSS feed and in the GHCNd status reports. Information on the dataset format, metadata, and definitions of the data quality, management, and source flags that accompany each datum is provided in this readme file.

NCEI recieves GHCN-D source data from National Meteorological and Hydrological Centers (NMHCs) around the world, through official bi-lateral agreements, and from a number of data archives at NCEI. These sources fall into four broad categories:

The U.S. Collection contains daily data from a dozen separate datasets archived at NCEI. These archives provide some of the earliest observations available for the United States (from the U.S. Forts and Voluntary Observer Program covering much of the 19th century) to the latest measurements from state-of-the-art climate monitoring stations that make up the U.S. Climate Reference Network. GHCNd contains the most complete collection of U.S. daily climate summaries available.

GHCNd is continually reprocessed. Because receipt of changes/additions occurs even for archive-quality data sources, a data value and/or quality flag is subject to change even after all archive quality sources are integrated. However, changes to U.S. data values are rare 60 days after the close of a particular data month.

The International Collection contains historical records for approximately 20,000 locations outside the U.S. from more than 100 different countries and largely reflects the data collection efforts that led to the release of Global Daily Climatology Network dataset (the predecessor to GHCNd). The summaries from some countries in this collection are historical and are not updated on a regular basis. As an example, precipitation records ended generally in the late 1990s for Brazil and South Africa, and in 1970 for India.

Government Exchange Data refers to data collected through official Global Climate Observing Systems (GCOS) and bilateral agreements. In the best case scenario, an NMHC may offer its complete digital daily climate database for inclusion in GHCNd, which is the case for Canada (with more than 7500 station records provided) and Australia (with more than 17,000 station records). In other cases, NMHCs have provided daily data only for the GCOS Surface Network stations under their jurisdiction, resulting in data from only a few stations. However, new historical contributions are periodically added to this collection.

Of the more than 100,000 stations that comprise GHCNd, approximately 20,000 updates with observations are during any given 30-day period.. While many sites report only precipitation, daily maximum and minimum temperatures are also available from more than 25,000 sites, and many stations in North America also report snowfall and snow depth. Snow depth observations for stations outside of the United States originate from the synoptic reports in the Global Summary of the Day dataset.

Like GHCNm, the concentration of stations with observations of temperature or precipitation in GHCNd is denser over North America and Eurasia than in Africa, Antarctica, and South America. However, the densest historical station networks in GHCNd come from the U.S., Canada, and Australia, which is a reflection of the comprehensive contributions from these countries. Nevertheless, Brazil, India, and South Africa have also contributed records from very dense national precipitation networks.

The temporal evolution of the station network is such that daily summaries are available from a relatively small number of stations before 1890 when the number of stations reporting maximum and minimum temperatures (precipitation) was about 2.5% (8.9%) of the peak number. The total number, spatial distribution, and temporal completeness generally increase through time for all variables, although both the temperature and precipitation networks attained their maximum density in the 1960s. For the periods of record of individual stations and elements, see the GHCNd inventory.

The process performs the first two of these steps whenever a new source dataset or additional stations become available, while the mingling of data is part of the automated processing that creates GHCNd on a regular basis.

The next step is to determine for each station in the source dataset if data for the same location are already contained in GHCNd, or if the station represents a new site. Whenever possible, stations are matched on the basis of network affiliation and station identification number. If no match exists, then there is consultation from different networks for existing cross-referenced lists that identify the correspondence of station identification numbers.

For example, data for Alabaster Shelby County Airport, Alabama, USA, is stored under Cooperative station ID 010116 in NCEI's datasets 3200 and 3206 as well as in the data stream from the High Plains Regional Climate Center; they are combined into one GHCNd record based on the ID. In data set 3210 and the various sources for ASOS stations, however, the data for this location are stored under WBAN ID 53864 and must be matched with the corresponding Cooperative station ID using NCEI's Master Station History Record.

A third approach is to match stations on the basis of their names and location. This strategy is more difficult to automate than the other two approaches because identification of multiple stations within the same city or town, with the same name and small differences in coordinates, can be the result of either differences in accuracy or the existence of multiple stations in close proximity to each other. As a result, the employment of the third approach is used only when stations cannot be matched on the basis of station identification numbers or cross-reference information. This is the case, for example, when there is a need for matching stations outside the U.S. whose data originate from the Global Summary of the Day dataset and from the International Collection.

The implementation of the above classification strategies yields a list of GHCNd stations and an inventory of the source datasets for integration of each station. This list forms the basis for integrating, or mingling, the data from the various sources to create GHCNd. Mingling takes place according to a hierarchy of data sources and in a manner that attempts to maximize the amount of data included while also minimizing the degree to which data from sources with different characteristics are mixed. While the mingling of precipitation, snowfall, and snow depth are separate, consideration of maximum and minimum temperatures is performed together in order to ensure the temperatures for a particular station and day always originate from the same source. Data from the Global Summary of the Day dataset are used only if no observations are available from any other source for that station, month, and element. Among the other sources, consideration of each day is made individually; if an observation for a particular station and day is available from more than one source, GHCNd uses the observation from the most preferred source available.

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