The Historical Map and Chart Collection of the Office of Coast Survey contains over 35000 historical maps and charts from the mid 1700s up through the 2020s, including the final cancelled editions of NOAA's raster charts. These images are available for viewing or download through the image catalog at https://historicalcharts.noaa.gov/. The Collection includes some of the nation's earliest nautical charts, hydrographic surveys, topographic surveys, bathymetric maps, annual reports, coast pilots, geodetic surveys, city plans, and Civil War battle maps. The Collection is a rich primary historical archive and a testament to the artistry of copper plate engraving technology of the nineteenth and twentieth centuries. Notable offerings include maps of Vancouver's explorations, the "Wilkes Atlas" of the U.S. Whistler's Anacapa Island chart, an extensive Civil War collection, a large scale topographic series of Washington, D.C., city plans, the reengraving of the famous 1792 L'Enfant and Ellicott plan for Washington D.C., and many artistic perspective sketches that were once an integral part of hydrographic surveys and published charts.

The Historical Map and Chart Collection of the Office of Coast Survey contains over 20000 historical maps and charts from the mid 1700s through the late 1900s. These images are available for viewing or download through the image catalog at http://chartmaker.ncd.noaa.gov/csdl/ctp/abstract.htmThe Collection includes some of the nation's earliest nautical charts, hydrographic surveys, topographic surveys, geodetic surveys, city plans and Civil War battle maps. The Collection is a rich primary historical archive and a testament to the artistry of copper plate engraving technology of the nineteenth and twentieth centuries. Notable offerings include maps of Vancouver's explorations, the "Wilkes Atlas" of the U.S. Whistler's Anacapa Island chart, an extensive Civil War collection, a large scale topographic series of Washington, D.C., city plans, the reengraving of the famous 1792 L'Enfant and Ellicott plan for Washington D.C., and many artistic perspective sketches that were once an integral part of hydrographic surveys and published charts.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of Massachusetts, United States Of America. The time period coverage is from 330 to 51 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Note that 2013 and 2014 datasets are available for download in the attachment tab below.The journal article describing GHCN-Daily is: Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, doi:10.1175/JTECH-D-11-00103.1.Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. [indicate subset used following decimal, e.g. Version 3.12]. NOAA National Climatic Data Center. http://doi.org/10.7289/V5D21VHZ

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of United States Of America. The time period coverage is from 144 to 76 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Dewberry contracted TRE Altamira Inc (TREA) for a historical InSAR analysis of ground displacement over the East Coast of the United States of America (U.S.A.), for the National Oceanic and Atmospheric Administration (NOAA). The area of interest (AOI) includes the major coastal cities and coastal plains and covers over 500,000 km2 (193,000 mi2). TREAs proprietary SqueeSAR algorithm was used to process Sentinel-1 (SNT) satellite imagery acquired between January 2017 and July 2023 and the results were calibrated using the NOAA's CORS GNSS (Global Navigation Satellite System) network for the generation of vertical InSAR (SAR Interferometry) measurements.

To preserve NEFSC historical data, images of biological and oceanographic data sheets (1948-1975) were scanned to digital format and can be queried through a portal on the NEFSC website. Images may include: cruise instructions, cruise tracks, original trawl logs, length frequency data sheets, cruise notes, tagging information and fisherman reports.

The U.S. Historical Climatology Network Monthly Data, Version 2.5 consists of precipitation and temperature data "corrected" for changes in station location, instrumentation, and observing practices. The vast majority of stations are from the NOAA Cooperative Observer Program (COOP) Network. Stations have been selected according to coverage, length of data record and completeness, and historical stability. Data includes sets of Maximum, Minimum and Average Temperature and Precipitation data that are either (1) raw (non-adjusted though flagged for possible quality issues), (2) adjusted due to time of observation bias (TOB) or (3) put through the Pairwise Homogenization Algorithm (PHA). The data also is archived with station information and source code for reading the data.

The Global Historical Tsunami Database provides information on over 2,400 tsunamis from 2100 BC to the present in the the Atlantic, Indian, and Pacific Oceans; and the Mediterranean and Caribbean Seas. The database includes two related files. The first file includes information on the tsunami source such as the date, time, and location of the source event; cause and validity of the source, tsunami magnitude and intensity; maximum water height; the total number of fatalities, injuries, houses destroyed, and houses damaged; and total damage estimate (in U.S. dollars). The second related file contains information on the runups (the locations where tsunami waves were observed by eyewitnesses, reconnaissance surveys, tide gauges, and deep-ocean sensors) such as name, location, arrival time, maximum water height and inundation distance, and socio-economic data (deaths, injuries, damage) for the specific runup location.

This data set contains mean monthly temperatures and total monthly precipitation for stations in Alaska from the mid-1800s to 1990. The values are a subset of the Global Historical Climatology Network (GHCN), archived at Oak Ridge National Laboratory.

NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).

Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.

Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.

Data include: source location, date, and time, event magnitude, maximum water height, total number of deaths, injuries and damage for the event.

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). The Global Historical Climatology Network - Daily (GHCN-Daily) dataset addresses the need for historical daily records over global land areas. Like its monthly counterpart (GHCN-Monthly), GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The meteorological elements measured for the data set include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. GHCN-Daily serves as the official archive for daily data from the Global Climate Observing System (GCOS) Surface Network (GSN) and is particularly well suited for monitoring and assessment activities related to the frequency and magnitude of extremes. Sources for the GHCN-Daily data set include, but are not limited, to U.S. Cooperative Summary of the Day, U.S. Fort data, U.S. Climate Reference Network, Community Collaborative Rain, Hail and Snow Network, and numerous international sources. The dataset contains measurements from over 75,000 stations worldwide,about two thirds of which are for precipitation measurement only. Approximately 8500 are regularly updated with observations from within the last month. While most of these sites report precipitation, daily maximum and minimum temperatures are available at more than 25,000 of them, and over 24,000 contain records of snowfall and/or snow depth. The process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: screening the source data for stations whose identity is unknown or questionable; classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and mingling the data from the different sources. The first two of these steps are performed whenever a new source dataset or additional stations become available, while the actual mingling of data is part of the automated processing that creates GHCN-Daily on a regular basis. GHCN-Daily data are subject to a suite of quality assurance checks. The checks consist of several types of carefully evaluated tests that detect duplicated data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of flagged values was used to set the threshold for each procedure such that the tests false-positive rate is minimized. In addition, the tests are performed in a deliberate sequence in an effort to enhance the performance of the later checks by detecting errors with the checks applied earlier in the sequence.

These data were automated to provide an accurate high-resolution historical shoreline of Delaware River, PA suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attrib...

The NOAA Voices Oral History Archives (VOHA) seeks to document the human experience as it relates to the changing environment, climate, oceans and coasts and other key areas of NOAAs work through firsthand oral history accounts from across the US and its territories. Oral histories contribute to NOAA's Mission of "Science, Service, and Stewardship" by creating, compiling, archiving and sharing the experiences of stakeholders, scientists and others. Any individual or organization can participate in the VOHA program by contributing individual oral history interviews or collections of interviews that are related to the project scope and mission, or by using the interviews archived here in their research, scholarship, exhibits, or general use. We accept oral histories produced by NOAA staff (including social scientists, historians, and others) as well as from from external organizations, universities, researchers and oral history practitioners. This content is made available to the public in this digital repository for educational and research purposes. The Voices Oral History Archives database is a powerful resource available to the public to inform, educate, and provide primary information for researchers interested in our local, human experience associated with the varied facets of NOAA's mission (including but not limited to Climate, Fisheries, Weather, Heritage, etc...)

Abstract

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.

Methodology

The process of integrating data from multiple sources into GHCNd takes place in three steps:

1. Screening the source data for stations whose identity is unknown or questionable
2. Classifying each station in a source dataset either as one that is already represented in GHCN-D or as a new site
3. Mingling the data from the different sources

%3C!-- --%3E

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.

Screening the Source Data

A station within a source dataset is considered for inclusion in GHCNd if it meets all of the following conditions:

• It can be identified with a name, latitude, and longitude contained in metadata provided as part of the source dataset or in standard station history information
• Its record contains 100 or more values for at least one of the GHCNd elements
• It does not fail the interstation duplicate check that compares records from all stations within a source dataset in order to identify cases in which more than 50% of a station's record is identical to the data from another station

%3C!-- --%3E

Classifying Stations

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.

Mingling

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.

Several criteria are used for the hierarchy of data sources used in cases of overlap. In gener

The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. Moored buoys are the weather sentinels of the sea. They are deployed in the coastal and offshore waters from the western Atlantic to the Pacific Ocean around Hawaii, and from the Bering Sea to the South Pacific. NDBC's moored buoys measure and transmit barometric pressure; wind direction, speed, and gust; air and sea temperature; and wave energy spectra from which significant wave height, dominant wave period, and average wave period are derived. Even the direction of wave propagation is measured on many moored buoys. See https://www.ndbc.noaa.gov/measdes.shtml for a description of the measurements.

The source data from NOAA NDBC has different column names, different units, and different missing values in different files, and other problems (notably, lots of rows with duplicate or different values for the same time point). This dataset is a standardized, reformatted, and lightly edited version of that source data, created by NOAA NMFS SWFSC ERD (email: erd.data at noaa.gov). Before 2020-01-29, this dataset only had the data that was closest to a given hour, rounded to the nearest hour. Now, this dataset has all of the data available from NDBC with the original time values. If there are multiple source rows for a given buoy for a given time, only the row with the most non-NaN data values is kept. If there is a gap in the data, a row of missing values is inserted (which causes a nice gap when the data is graphed). Also, some impossible data values are removed, but this data is not perfectly clean. This dataset is now updated every 5 minutes.

This dataset has both historical data (quality controlled, before 2023-12-01T00:00:00Z) and near real time data (less quality controlled, which may change at any time, from 2024-01-01T00:00:00Z on). cdm_data_type=TimeSeries cdm_timeseries_variables=station, longitude, latitude contributor_name=NOAA NDBC contributor_role=Source of data. Conventions=COARDS, CF-1.6, ACDD-1.3, NCCSV-1.2 Easternmost_Easting=179.001 featureType=TimeSeries geospatial_lat_max=71.758 geospatial_lat_min=-55.0 geospatial_lat_units=degrees_north geospatial_lon_max=179.001 geospatial_lon_min=-177.75 geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_units=m history=Around the 25th of each month, erd.data@noaa.gov downloads the latest yearly and monthly historical .txt.gz files from https://www.ndbc.noaa.gov/data/historical/stdmet/ and generates one historical .nc file for each station. erd.data@noaa.gov also downloads all of the 45day near real time .txt files from https://www.ndbc.noaa.gov/data/realtime2/ and generates one near real time .nc file for each station. Every 5 minutes, erd.data@noaa.gov downloads the list of latest data from all stations for the last 2 hours from https://www.ndbc.noaa.gov/data/latest_obs/latest_obs.txt and updates the near real time .nc files. id=cwwcNDBCMet infoUrl=https://www.ndbc.noaa.gov/ institution=NOAA NDBC, NOAA NMFS SWFSC ERD keywords_vocabulary=GCMD Science Keywords naming_authority=gov.noaa.pfeg.coastwatch Northernmost_Northing=71.758 project=NOAA NDBC and NOAA NMFS SWFSC ERD quality=Automated QC checks with periodic manual QC source=station observation sourceUrl=https://www.ndbc.noaa.gov/ Southernmost_Northing=-55.0 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=station, longitude, latitude testOutOfDate=now-25minutes time_coverage_end=2025-06-28T21:10:00Z time_coverage_start=1972-10-01T00:00:00Z Westernmost_Easting=-177.75

description: National Oceanic and Atmospheric Administration (NOAA) Climate Data Records (CDR) provide historical climate information using data from weather satellites. This dataset contains daily Normalized Difference Vegetation Index (NDVI) derived from surface reflectance data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor. This long-term record spans from 1981 to 2013 and utilizes AVHRR data from seven NOAA polar orbiting satellites: NOAA 7, 9, 11, 14, 16, and 18. This NDVI collection provides the global change and resource management communities with vegetation data for historical trend analysis and vegetation monitoring studies for land surfaces around the globe.; abstract: National Oceanic and Atmospheric Administration (NOAA) Climate Data Records (CDR) provide historical climate information using data from weather satellites. This dataset contains daily Normalized Difference Vegetation Index (NDVI) derived from surface reflectance data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor. This long-term record spans from 1981 to 2013 and utilizes AVHRR data from seven NOAA polar orbiting satellites: NOAA 7, 9, 11, 14, 16, and 18. This NDVI collection provides the global change and resource management communities with vegetation data for historical trend analysis and vegetation monitoring studies for land surfaces around the globe.

This web map was developed to show the geographic distribution of the oral history interviews contained within the archive of the NOAA Voices program. This map is used in the NOAA Voices Oral History Interview Mapping Application, found here: https://noaa.maps.arcgis.com/home/item.html?id=a220357bec444ab0be7e586fb5ecd26eEach interview is treated as a separate data point with a variety of attributes. These attributes include: narrator, interviewer, date of interview, city, state, interviewer, project, link to interview, and interview description.Each point in this dataset is plotted at the city level. The size of these points is directly tied to the number of interviews within that location.The data and metadata for this application can be found on the NOAA Voices website, here: https://voices.nmfs.noaa.gov/. Each interview has its own landing page on the NOAA Voices site, and the information on these landing pages mirrors the data in this application.

Fire burnt areas prior to October 2022 mapped manually every 9 days from 1km resolution NOAA/AVHRR Satellite Imagery. Now automatically detected daily from 375 m VIIRS satellite imagery since October 2022 and visually QAed on a weekly basis. Coarse resolution not suitable for all land management applications. Not all fires will be mapped. Fires under 4 km², Persistent cloud cover, vegetation canopy will not be mapped. Show full description