OSU_SnowCourse Summary: Manual snow course observations were collected over WY 2012-2014 from four paired forest-open sites chosen to span a broad elevation range. Study sites were located in the upper McKenzie (McK) River watershed, approximately 100 km east of Corvallis, Oregon, on the western slope of the Cascade Range and in the Middle Fork Willamette (MFW) watershed, located to the south of the McKenzie. The sites were designated based on elevation, with a range of 1110-1480 m. Distributed snow depth and snow water equivalent (SWE) observations were collected via monthly manual snow courses from 1 November through 1 April and bi-weekly thereafter. Snow courses spanned 500 m of forested terrain and 500 m of adjacent open terrain. Snow depth observations were collected approximately every 10 m and SWE was measured every 100 m along the snow courses with a federal snow sampler. These data are raw observations and have not been quality controlled in any way. Distance along the transect was estimated in the field. OSU_SnowDepth Summary: 10-minute snow depth observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meterological stations were located in the approximate center of each forest or open snow course transect. These data have undergone basic quality control. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN flags for missing data to NA, and added site attributes such as site name and cover. We replaced positive values with NA, since snow depth values in raw data are negative (i.e., flipped, with some correction to use the height of the sensor as zero). Thus, positive snow depth values in the raw data equal negative snow depth values. Second, the sign of the data was switched to make them positive. Then, the smooth.m (MATLAB) function was used to roughly smooth the data, with a moving window of 50 points. Third, outliers were removed. All values higher than the smoothed values +10, were replaced with NA. In some cases, further single point outliers were removed. OSU_Met Summary: Raw, 10-minute meteorological observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meteorological stations were located in the approximate center of each forest or open snow course transect. These stations were deployed to collect numerous meteorological variables, of which snow depth and wind speed are included here. These data are raw datalogger output and have not been quality controlled in any way. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN and 7999 flags for missing data to NA, and added site attributes such as site name and cover. OSU_Location Summary: Location Metadata for manual snow course observations and meteorological sensors. These data are compiled from GPS data for which the horizontal accuracy is unknown, and from processed hemispherical photographs. They have not been quality controlled in any way.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

The data in this resource consist of biodiversity occurrence records drawn from the National Biodiversity Data Bank's database. The data was clipped using the Albertine Rift boundaries and mapped to Darwin Core terms by the NBDB's manager, Dr Herbert Tushabe.

This archive contains raw observations of the 2009-10-09 impact of the LCROSS spacecraft on the moon by the CLIO instrument on the MMT Observatory 6.5m telescope. The archive consists of uncalibrated FITS images of the event. This is one of several data sets of Earth-based observations of the impact.

This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.

The differences between the observations and the forecast background used for the analysis (the innovations or O-F for short) and those between the observations and the final analysis (O-A) are by-products of any assimilation system and provide information about the quality of the analysis and the impact of the observations. Innovations have been traditionally used to diagnose observation, background and analysis errors at observation locations (Hollingsworth and Lonnberg 1989; Dee and da Silva 1999). At the most simplistic level, innovation variances can be used as an upper bound on background errors, which are, in turn, an upper bound on the analysis errors. With more processing (and the assumption of optimality), the O-F and O-A statistics can be used to estimate observation, background and analysis errors (Desroziers et al. 2005). They can also be used to estimate the systematic and random errors in the analysis fields. Unfortunately, such data are usually not readily available with reanalysis products. With MERRA, however, a gridded version of the observations and innovations used in the assimilation process is being made available. The dataset allows the user to conveniently perform investigations related to the observing system and to calculate error estimates. Da Silva (2011) provides an overview and analysis of these datasets for MERRA. The innovations may be thought of as the correction to the background required by a given instrument, while the analysis increment (A-F) is the consolidated correction once all instruments, observation errors, and background errors have been taken into consideration. The extent to which the O-F statistics for the various instruments are similar to the A-F statistics reflects the degree of homogeneity of the observing system as a whole. Using the joint probability density function (PDF) of innovations and analysis increments, da Silva (2011) introduces the concepts of the effective gain (by analogy with the Kalman gain) and the contextual bias. In brief, the effective gain for an observation is a measure of how much the assimilation system has drawn to that type of observation, while the contextual bias is a measure of the degree of agreement between a given observation type and all other observations assimilated. With MERRAs gridded observation and innovation data sets, a wealth of information is available for examination of the quality of the analyses and how the different observations impact the analyses and interact with each other. Such examinations can be conducted regionally or globally and should provide useful information for the next generation of reanalyses.

Various input data were used by the 20th Century Reanalysis project team including surface pressure observations from the International Surface Pressure Databank version 2. The original observations from the International Surface Pressure Databank version 2 used in the project are archived with associated original metadata and model quality control information. These are the only records which have the original station metadata and source information and model quality control information on the same record. The 20th Century Reanalysis dataset provides 6 hourly analyses on a Global 2.0 degree latitude x 2.0 degree longitude global grid from 1871 to present produced from a series of 56-member ensemble runs. For each output time step ensemble means and associated spreads have been calculated and form a continuous dataset over the entire timespan of the dataset. The data contain fields at the surface and on pressure levels from 1000 hPa to 10 hPa and are also available as part of the copy held by the British Atmospheric Data Centre. The dataset authors request that the following acknowledgment be included in all papers using the dataset: 'Support for the Twentieth Century Reanalysis Project dataset is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE - http://www.doeleadershipcomputing.org/incite-program/ ) program, and Office of Biological and Environmental Research (BER - http://science.energy.gov/ber/ ), and by the National Oceanic and Atmospheric Administration Climate Program Office (http://www.climate.noaa.gov/).'

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 16 threshold values of precipitation for the contiguous United States for 1950-2023. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2023. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

Climate Data Description

Dataset Title: A climate data record of atmospheric moisture and sea surface temperature from satellite observations

Data Period: June 2002 to May 2022

Spatial Resolution: 0.25° × 0.25° grid

Data Variables: CWV (Column Water Vapor) and SST (Sea Surface Temperature)

1. Dataset Overview

This climate data record (CDR) provides monthly mean values of two essential climate variables: CWV and SST, covering the period from June 2002 to May 2022. The data is presented at a spatial resolution of 0.25° × 0.25° longitude-latitude grid, providing global ocean coverage.

2. Data Source

The raw data used to develop the CDR of CWV and SST were derived from observations by three satellite instruments with close local observation time. These instruments are:

§ AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) onboard NASA’s Aqua satellite;

§ AMSR2 (Advanced Microwave Scanning Radiometer-2) onboard JAXA’s Global Change Observation Mission first-Water (GCOM-W1) satellite;

§ MWRI (MicroWave Radiometer Imager) onboard the FengYun-3B (FY3B) satellite, developed by the National Satellite Meteorological Center (NSMC) of the China Meteorological Administration.

3. Variable Description

CWV: CWV represents the total amount of water vapor in a column of air, measured in units of mm (or kg/m²). This variable affects the global water cycle, particularly the frequency and severity of heavy precipitation and drought events, which have significant impacts on human life and economies.

SST: SST refers to the temperature of the ocean's surface, measured in units of K. SST is a key indicator of human-induced global warming. Changes in SST also influence the ocean-atmosphere exchange of sensible and latent heat fluxes, as well as atmospheric dynamic and thermodynamic processes, through ocean-atmosphere coupling.

4. File Format

The file name is “Mean_CWV_SST_CDR_S200206_E202205_C202412.nc”, where “S200206”, “E202205”, and “C202412” correspond to the start date, end date, and creation date, respectively.

The dataset is provided in NetCDF format (.nc), a widely-used format for climate and atmospheric data. Each file contains the following variables (dimensions):

§ time (240): the days since 2000-01-01;

§ lon (1440): longitude, unites: degree east;

§ lat (721): latitude, units: degree north;

§ cwv (240,1440,721): Column Water Vapor, units: mm;

§ sst (240,1440,721): Sea Surface Temperature, units: K.

Last Revised: February 2016 Map InformationThis nowCOAST™ time-enabled map service provides maps depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is a method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in; however, all cloud cover values are presently displayed using the "Missing" symbol due to a problem with the source data. Present weather information is also not available for display at this time. Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs, which indicate wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds.Due to software limitations, the observations included in this map service are organized into three separate group layers: 1) Wind velocity (wind barb) observations, 2) Cloud Cover observations, and 3) All other observations, which are displayed as numerical values (e.g. Air Temperature, Wind Gust, Visibility, Sea Surface Temperature, etc.).Additionally, due to the density of weather/ocean observations in this map service, each of these group data layers has been split into ten individual "Scale Band" layers, with each one visible for a certain range of map scales. Thus, to ensure observations are displayed at any scale, users should make sure to always specify all ten corresponding scale band layers in every map request. This will result in the scale band most appropriate for your present zoom level being shown, resulting in a clean, uncluttered display. As you zoom in, additional observations will appear.The observations in this nowCOAST™ map service are updated approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observations for a particular station may update only once per hour. For more detailed information about layer update frequency and timing, please reference the nowCOAST™ Dataset Update Schedule.Background InformationThe maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing stations from the U.S.A. and other countries. For terrestrial networks, the platforms include but are not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Real-Time System (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until approximately 23 minutes past top of the hour for land-based stations and 33 minutes past the top of the hour for maritime stations.Time InformationThis map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:Issue a returnUpdates=true request (ArcGIS REST protocol only) for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of the REST Service page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes referred to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST™ LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST™ LayerInfo Help DocumentationReferencesNWS, 2013: Sample Station Plot, NWS/NCEP/WPC, College Park, MD (Available at http://www.wpc.ncep.noaa.gov/html/stationplot.shtml).NWS, 2013: Terminology and Weather Symbols, NWS/NCEP/OPC, College Park, MD (Available at http://www.opc.ncep.noaa.gov/product_description/keyterm.shtml).NWS, 2013: How to read Surface weather maps, JetStream an Online School for Weather (Available at http://www.srh.noaa.gov/jetstream/synoptic/wxmaps.htm).

These NCEP ADP operational global synoptic surface data reports were collected from the GTS during time slots centered on the 6-hourly analysis times of their global and regional models. Two types of data were collected and the reports usually include pressure, temperature, dew point depression, wind direction and speed. NCEP also decoded the precipitation data, but only for the U.S. and Canada. The two types are: 1) data from land stations, including SYNOP, METAR, and beginning in the mid 1990s, AWOS and ASOS reports, and 2) data from moving ship, fixed ship, MARS (moving and fixed), and buoys (moored and drifting).

Radar Weather Observation is a set of archived historical manuscripts stored on microfiche. The primary source of these radar weather observations manuscript records was approximately 110 National Weather Service (NWS) radar stations. The records were placed on microfiche monthly or annually, depending on the radar station. In addition, the years these records are available vary from station to station. There were approximately 59 NWS radar observing stations equipped with weather surveillance radar which made photographs of the plan position indicator (PPI) scope on 16-mm or 35-mm film. When echoes were visible, pictures were taken at least every 5 minutes and sometimes as often as every 40 seconds. The manuscripts consist of hourly and special observations on daily MF 7-60 forms. Observations were taken about 35 minutes past the hour throughout each 24 hour period. Each observation provides detailed information about the character, type, and intensity of precipitation; direction and distance of the echoes from the station; movement of echoes; maximum height of cells; and pertinent remarks. Most stations discontinued providing the manuscript data in 1996, but there are a few stations with data available after this time.

description: The National Ocean Service (NOS) maintains a long-term database containing a listing of active stations that are installed all over the United States and U.S. territories. Water Levels observations, coastal currents and other meteorological and oceanographic data are monitored from a network of over 200 permanent, continuously operating stations and from numerous stations operated for short-term and long-term projects. Stations are configured for a variety of observation periods, depending upon the location. For some stations, records date back to the late 1800s. Observed water level, ancillary and meteorological values are disseminated primarily at six minute increments. In addition, some stations provide real-time data for planning and emergency situations. The observed values are processed, stored and distributed over a wide variety of applications. The products are distributed on either hard copy, floppy disk, CD, or over the web and include the following: Active Station Lists , Tide Observation , Wind data , Air Temperature data , Water Temperature data , Barometric Pressure data , Conductivity data , Humidity data , Air Gap data , Currents data , Other Meteorological and or ancillary data. The National Oceanic and Atmospheric Administration (NOAA)/ National Ocean Service's (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) manages the National Current Observation Program (NCOP) to collect, analyze, and distribute observations and predictions of currents. The program's goals are to ensure safe, efficient and environmentally sound maritime commerce, and to support environmental needs such as HAZMAT response. The principal product generated by this program is information used to maintain and update the Tidal Current Tables.; abstract: The National Ocean Service (NOS) maintains a long-term database containing a listing of active stations that are installed all over the United States and U.S. territories. Water Levels observations, coastal currents and other meteorological and oceanographic data are monitored from a network of over 200 permanent, continuously operating stations and from numerous stations operated for short-term and long-term projects. Stations are configured for a variety of observation periods, depending upon the location. For some stations, records date back to the late 1800s. Observed water level, ancillary and meteorological values are disseminated primarily at six minute increments. In addition, some stations provide real-time data for planning and emergency situations. The observed values are processed, stored and distributed over a wide variety of applications. The products are distributed on either hard copy, floppy disk, CD, or over the web and include the following: Active Station Lists , Tide Observation , Wind data , Air Temperature data , Water Temperature data , Barometric Pressure data , Conductivity data , Humidity data , Air Gap data , Currents data , Other Meteorological and or ancillary data. The National Oceanic and Atmospheric Administration (NOAA)/ National Ocean Service's (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) manages the National Current Observation Program (NCOP) to collect, analyze, and distribute observations and predictions of currents. The program's goals are to ensure safe, efficient and environmentally sound maritime commerce, and to support environmental needs such as HAZMAT response. The principal product generated by this program is information used to maintain and update the Tidal Current Tables.

This part of USGS Data Series 935 (Cochrane, 2014) presents observations from underwater video collected in the Offshore of Seattle, California, map area, a part of the Southern Salish Sea Habitat Map Series. To validate the interpretations of multibeam sonar data and turn it into geologically and biologically useful information, the U.S. Geological Survey (USGS) towed a camera sled over specific locations throughout the Seattle map area to collect video and photographic data that would “ground truth” the seafloor. The ground-truth survey conducted in the Offshore of Seattle map area occurred in 2011 on the R/V Karluk (USGS field activity K0111PS) and on the Washington State Department of Fish and Game R/V Molluscan (USGS field activity M0111PS). The underwater camera sled was towed 1 to 2 m above the seafloor at speeds of between 1 and 2 nautical miles/hour. The surveys for this map area include approximately 6 hours (9.1 trackline km) of video.

archive

Daily weather observations from global land stations, recorded in synoptic code. Period of record 1950 only.

Here we present raw temperatures from Underwater Temperature Records (UTRs) located at a depth of 18m off Sodwana Bay (27.41483°S 32.72667°E), along the east coast of South Africa, between 14 March 1997 and 07 June 1997. Note that the data that falls outside of these dates is not from the deployment. At selected sites around Southern Africa, UTRs have been used to obtain long-term records of bottom temperature in the nearshore environment, at depths ranging from 2m to 34m.

Here we present raw temperatures from Underwater Temperature Recorders (UTRs) located at a depth of 2.5m off Elands Bay (32.3167°S 18.3200°E), along the west coast of South Africa, between 20 July 1993 and 26 August 1993. Note that the data that falls outside of these dates is not from the deployment. At selected sites around Southern Africa, UTRs have been used to obtain long-term records of bottom temperature in the nearshore environment, at depths ranging from 2m to 34m.

This dataset is about: Meteorological observations during MERSEY cruise from Bermuda to Staten Island started at 1821-05-08. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.611088 for more information.

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.