Updates to Website: (Please add new items at the top of this description with the date of the website change) May 9, 2012: Uploaded experimental data in matlab format for HIRENASD November 8, 2011: New grids, experimental data for HIRENASD configuration, new FEM for HIRENASD configuration. (JHeeg) Oct 13: Uploaded BSCW grids (VGRID) (PChwalowski) Oct 5: Added HIRENASD experimental data for test points #159 and #132 (JHeeg, PChwalowski)

📊 Dataset README (Updated with Temporal Coverage) 📈 Overview 🌐 This README document provides detailed information about a dataset that combines temperature 🌡️ and rainfall 🌧️ data. The temperature data is sourced from NASA's POWER Project, and the rainfall data is obtained from the Humanitarian Data Exchange (HDX) website, specifically focusing on Bangladesh rainfall data. Temperature Data Source 🔥 Source: NASA's POWER (Prediction of Worldwide Energy Resources) Data Access Viewer URL: NASA POWER Data Access Viewer Description: The POWER Project provides solar and meteorological data sets, primarily intended for renewable energy, sustainable buildings, agriculture, and other related applications. The temperature data from this source is a part of NASA's global meteorological data. Rainfall Data Source 🌧️ Source: Humanitarian Data Exchange (HDX) URL: Bangladesh Rainfall Data - HDX Description: HDX hosts various humanitarian data including climate and weather-related datasets. The rainfall data for Bangladesh is part of their collection, providing detailed subnational rainfall statistics. Dataset Description 📝 Composition 📊 The dataset is a combination of the temperature and rainfall data, aligned by date to facilitate joint analysis. The key components are: Temperature Data (tem): Represents the monthly average temperature, presumably in degrees Celsius. Rainfall Data (rain): Indicates monthly total rainfall, presumably measured in millimeters. Structure 🏗️ The dataset is structured into a CSV file with the following columns: tem: Average temperature for the month. Month: The month for the data point, ranging from 1 (January) to 12 (December). Year: The year of the data point. rain: Total rainfall for the month. Temporal Coverage 📆 Earliest Date: 1901 Latest Date: 2023 This dataset provides a historical perspective on climate trends from the earliest year of 1901 to the most recent data available up to 2023. Usage and Applications 🚀 This dataset is particularly useful for studying climatic patterns, seasonal changes, and long-term climate trends. Applications include but are not limited to: Climatological research and climate change studies. Agricultural planning and forecasting. Environmental and ecological studies. Resource management and planning in sectors sensitive to climatic variations. Limitations and Considerations 🚧 Geographical Specificity: The rainfall data is specific to Bangladesh and may not represent global patterns. Data Integration: The temperature and rainfall data come from two different sources; users should consider potential discrepancies in data collection methods and accuracy. Updates and Maintenance 🔄 Data Update Frequency: Check the source websites for the update frequency and availability of more recent data. Last Updated: Refer to the source websites for the last update date of the data. Licensing and Usage Rights ©️ Users should refer to the respective source websites for information on licensing and usage rights. It is important to adhere to the terms and conditions set by the data providers. Contact Information 📞 For specific queries related to the temperature or rainfall data, users should contact the respective data providers through their official communication channels provided on their websites.

FLUXNET is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. This dataset provides information from the ORNL DAAC-hosted FLUXNET site database which was discontinued in 2016. The files provided contain a list of investigators associated with each tower site, site locations and environmental data, and a bibliography of papers that used FLUXNET data. For more up to date information on FLUXNET sites, see http://fluxnet.fluxdata.org/.

MPLNET is a global network of micro-pulse lidar (MPL) systems. The MPL system is a single channel (523nm), autonomous, eye-safe lidar originally developed at the NASA Goddard Space flight Center (GSFC) that is now available commercially. The MPL system is used to determine the vertical structure of clouds and aerosols in the atmosphere. MPL data are analyzed to produce optical properties such as extinction and optical depth profiles of the clouds and aerosols.

The primary goal of MPLNET is to provide long-term data sets of cloud and aerosol vertical distribution at key sites around the world. The long-term data sets will be used to validate and help improve global and regional climate models, and also serve as ground-truth sites for NASA/EOS satellite programs such as the Geoscience Laser Altimeter System (GLAS) [http://virl.gsfc.nasa.gov/glas/] on the ICESat spacecraft (launch date December 2002). MPLNET is run by members of the GSFC Cloud and Aerosol Lidar Group (Code 912) [http://virl.gsfc.nasa.gov/] and is funded by NASA/EOS. Additional funding for research cruises at sea is provided by the NASA SIMBIOS project [http://simbios.gsfc.nasa.gov/].

MPLNET data products are divided into the following levels: Level 0; Level 1; Level 1.5a; Level 1.5b; Level 2a; and Level 3a. All MPLNET data products are described at [http://mplnet.gsfc.nasa.gov/data_overview.html].

Data Policy: Use of any images and/or data from MPLNET sites, field experiments, and cruises must be made with the authorization of the site/experiment PI. An offer of co-authorship on any publications, presentations, etc. must be made to the PI and his/her team if images and/or data are used.

This layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP, NOAA-20, and NOAA-21 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1) or NOAA-21 satellite (2). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireHours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.Additional information can be found on the NASA FIRMS site FAQ.Note about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.RevisionsMarch 7, 2024: Updated to include source data from NOAA-21 Satellite.September 15, 2022: Updated to include 'Hours_Old' field. Time series has been disabled by default, but still available.July 5, 2022: Terms of Use updated to Esri Master License Agreement, no longer stating that a subscription is required!This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

FLUXNET is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. This dataset provides information from the ORNL DAAC-hosted FLUXNET site database which was discontinued in 2016. The files provided contain a list of investigators associated with each tower site, site locations and environmental data, and a bibliography of papers that used FLUXNET data. For more up to date information on FLUXNET sites, see http://fluxnet.fluxdata.org/.

This database table contains the list of all Röntgen Satellite (ROSAT) X-Ray Telescope (XRT) pointing-mode observations for which data sets are available, i.e., it excludes the ROSAT All-Sky Survey observations. Users should consult the RASSMASTER database table for those XRT observations which were made in scanning mode during the ROSAT All-Sky Survey (RASS) phase (30 July 1990 to 25 January 1991, and 3 August 1991 to 13 August 1991). For each observation listed in this table, parameters such as the focal-plane instrument used, the data processing site, and the target name and coordinates are given, as well as the ROSAT Observation Request (ROR) number, the actual and requested exposure times, the date(s) on which the observation took place, etc. For details about the ROSAT instruments, consult the ROSAT Guest Observer Facility (GOF) website at https://heasarc.gsfc.nasa.gov/docs/rosat/. A list of the available online ROSAT documentation can be found at https://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html. This table was created by the HEASARC in July 2004 by combining the data from two long-standing HEASARC Browse tables into one master table. It was updated by the HEASARC in March 2022 to add start and end times for the 157 sequence IDs which did not already have start and end times. This is a service provided by NASA HEASARC .

The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. Direct measurement approaches included periodic area harvest for non-forest sites and application of allometric equations to tree diameter data for forest sites. LAI was also estimated indirectly using the Li-Cor LAI-2000 Plant Canopy Analyzers (Gower et al. 1999). LAI was measured three times each year at the forest sites and four to six times at other sites depending upon the phenology of LAI development for a given ecosystem. To develop LAI surfaces at any given site, the Landsat ETM+ image closest in date to maximum LAI was chosen as a reference and images from other dates radiometrically normalized to it. Each LAI surface has a grain of 25 meters and covers a 7 x 7 km extent. The data set consists of the LAI surface images in standard geotiff format, an accompanying text file which provides metadata specific to the image (such as projection, data type, class names, etc), and associated auxiliary and world files. Additional information on LAI measurements and surface development can be found on the BigFoot website at http://www.fsl.orst.edu/larse/bigfoot/ovr_mthd.html. BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellite Terra (http://landval.gsfc.nasa.gov/MODIS/index.php), is used to produce several science products including land cover, leaf area index (LAI) and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high resolution remote sensing data, and ecosystem process models at nine flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 MODIS pixels) surrounding the CO2 flux towers located at each of the nine sites. We collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle. Our sampling design allowed us to examine scales and spatial pattern of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program.

This table contains the redshift catalog for the X-ray sources detected in the Chandra Deep Field-North (CDF-N). The catalog for the CDF-N includes redshifts from previous work. The authors have extended the redshift information for the full sample using photometric redshifts. The goal of the OPTX Project is to use this survey, together with the Chandra Large-Area Synoptic X-Ray Survey (CLASXS) and the Chandra Lockman Area North Survey (CLANS), which are among the most spectroscopically complete surveys to date, to analyze the effect of spectral type on the shape and evolution of the X-ray luminosity functions and to compare the optical spectral types with the X-ray spectral properties. The CLANS and CLASXS surveys bridge the gap between the ultra-deep pencil-beam surveys, such as the Chandra Deep Fields, and the shallower, very large-area surveys. This table also contains updated optical and infrared photometric data for the X-ray sources in the CDF-N. Typical photometric uncertainties are given in Section 3.6 of the reference paper (Trouille et al. 2008). The X-ray information for the sources detected in the CDF-N 2-megasecond exposure which was published in Alexander et al. (2003, AJ, 126, 539) is available as the HEASARC CHANDFN2MS table, while the earlier catalog which listed information about optical and infrared counterparts (Barger et al. 2003, AJ, 126, 632) is available as the HEASARC CDFN2MSOID table. This table was created by the HEASARC in January 2009 based on the electronic version of Table 13 from the paper which was obtained from the ApJ web site. This is a service provided by NASA HEASARC .

This data set provides landcover maps of (1) peatland type (bog, fen, marsh, swamp) with levels of biomass (open, forested) and (2) Burn Severity Index (BSI) (Dyrness and Norum, 1983) for four wildfire areas in northern Alberta, Canada. The four wildfire sites include the Utikuma fire site of 2011, Kidney Lake fire site of 2011, Fort McMurray west fire site of 2009, and Fort McMurray east fire site of 2009.

The peatland classification at 12.5-m resolution (fen vs. bog including treed vs. open vs. shrubby) at each wildfire site was based on a pre-burn 2007 multi-date, multi-sensor fusion (Optical-IR, C-band and L-band SAR) approach. Over 350 field locations were sampled in central Alberta to train and validate the peatland type maps. The additional site, Wabasca, was an unburned site.

Burn severity was measured in the field using the Burn Severity Index (BSI) (Dyrness and Norum 1987), a qualitative assessment of burnt moss that uses a 1-5 scale, with 1 being unburnt and 5 being severely burnt. The field data of ground consumption were correlated with Landsat pre- and post-burn imagery, specific to peatlands, to develop multivariate models for calculating burn severity and %-not-sphagnum-moss. These models were used to generate the Burn Severity Maps at 30-m resolution (percent unburned moss, and the burn severity index (BSI)).

All sites were visited in 2013 for field measurements and the Utikuma site was also visited in 2012 for field measurements. Additional biophysical data for the various peatlands (aboveground biomass – tree and shrub, plant heights, density, etc. were collected and will be provided in another data set.