Tornado TracksThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays tornadoes in the United States, Puerto Rico and U.S. Virgin Islands between 1950 and 2024. A tornado track shows the route of a tornado. Per NOAA, "A tornado is a narrow, violently rotating column of air that extends from a thunderstorm to the ground. Because wind is invisible, it is hard to see a tornado unless it forms a condensation funnel made up of water droplets, dust and debris. Tornadoes can be among the most violent phenomena of all atmospheric storms we experience. The most destructive tornadoes occur from supercells, which are rotating thunderstorms with a well-defined radar circulation called a mesocyclone. (Supercells can also produce damaging hail, severe non-tornadic winds, frequent lightning, and flash floods.)"EF-5 Tornado Track (May 3, 1999) near Oklahoma City, OklahomaData currency: December 30, 2024Data source: Storm Prediction CenterData modifications: Added field "Date_Calc"For more information: Severe Weather 101 - Tornadoes; NSSL Research: TornadoesSupport documentation: SPC Tornado, Hail, and Wind Database Format SpecificationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

This table contains a summary of the number of tornadoes by year for the United States. The table also provides summary statistics for fatalities, injuries, magnitude, and crop losses by hour. The data should be downloaded and used in a spreadsheet program like Excel, Numbers, or Google Sheets. Data is derived from Tornado data from the National Weather Service.

The data comes from NOAA's National Weather Service Storm Prediction Center Severe Weather Maps, Graphics, and Data Page

Data Dictionary

tornados.csv

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview - National Tornadoes provides a summary of tornadic activity in the United States. Tornado occurrences and significant events, including storms and outbreaks, are covered. Regular monthly and annual reports begin in July 2008. Spring "tornado seaso" reports are available for 2006 and 2008. In some months during climatologically inactive periods, the narrative part of this report may be omitted.

TornadoesThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays tornadoes in the United States, Puerto Rico and U.S. Virgin Islands between 1950 and 2024. Per NOAA, "A tornado is a narrow, violently rotating column of air that extends from a thunderstorm to the ground. Because wind is invisible, it is hard to see a tornado unless it forms a condensation funnel made up of water droplets, dust and debris. Tornadoes can be among the most violent phenomena of all atmospheric storms we experience. The most destructive tornadoes occur from supercells, which are rotating thunderstorms with a well-defined radar circulation called a mesocyclone. (Supercells can also produce damaging hail, severe non-tornadic winds, frequent lightning, and flash floods.)"EF-5 Tornado (May 22, 2011) near Joplin, MissouriData currency: December 30, 2024Data source: Storm Prediction CenterData modifications: Added field "Date_Calc"For more information: Severe Weather 101 - Tornadoes; NSSL Research: TornadoesSupport documentation: SPC Tornado, Hail, and Wind Database Format SpecificationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

Storm Data is provided by the National Weather Service (NWS) and contain statistics on personal injuries and damage estimates. Storm Data covers the United States of America. The data began as early as 1950 through to the present, updated monthly with up to a 120 day delay possible. NCDC Storm Event database allows users to find various types of storms recorded by county, or use other selection criteria as desired. The data contain a chronological listing, by state, of hurricanes, tornadoes, thunderstorms, hail, floods, drought conditions, lightning, high winds, snow, temperature extremes and other weather phenomena.

• Update Frequency: Weekly

Data from this dataset can be downloaded/accessed through this dataset page and Kaggle's API.

Context

Severe weather is defined as a destructive storm or weather. It is usually applied to local, intense, often damaging storms such as thunderstorms, hail storms, and tornadoes, but it can also describe more widespread events such as tropical systems, blizzards, nor'easters, and derechos.

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. The records in SWDI come from a variety of sources in the NCDC archive. SWDI provides the ability to search through all of these data to find records covering a particular time period and geographic region, and to download the results of your search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML.

Content

The current data layers in SWDI are:
- Filtered Storm Cells (Max Reflectivity >= 45 dBZ) from NEXRAD (Level-III Storm Structure Product)
- All Storm Cells from NEXRAD (Level-III Storm Structure Product)
- Filtered Hail Signatures (Max Size > 0 and Probability = 100%) from NEXRAD (Level-III Hail Product)
- All Hail Signatures from NEXRAD (Level-III Hail Product)
- Mesocyclone Signatures from NEXRAD (Level-III Meso Product)
- Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product)
- Tornado Signatures from NEXRAD (Level-III TVS Product)
- Preliminary Local Storm Reports from the NOAA National Weather Service
- Lightning Strikes from Vaisala NLDN

Disclaimer:
SWDI provides a uniform way to access data from a variety of sources, but it does not provide any additional quality control beyond the processing which took place when the data were archived. The data sources in SWDI will not provide complete severe weather coverage of a geographic region or time period, due to a number of factors (eg, reports for a location or time period not provided to NOAA). The absence of SWDI data for a particular location and time should not be interpreted as an indication that no severe weather occurred at that time and location. Furthermore, much of the data in SWDI is automatically derived from radar data and represents probable conditions for an event, rather than a confirmed occurrence.

Acknowledgements

Dataset Source: NOAA. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Cover photo by NASA on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

We provide a dataset of tornadoes and waterspouts in Chile from 1554 to present based in chronicles, newspaper articles, social media, scientific literature and books. The database includes only those events that have been qualified as more than likely a tornado or waterspout based on a subjective qualification by the researchers. For each tornado we provide at least one geographical location point, the local and UTC hour (if known) and for most cases an estimation of the intensity based on the Enhanced Fujita damage scale.The following are the parameters contained in the database:N°: This is the entry number or identifier for each record in the file.Location: The name of the place where the weather event occurred.Latitude: The geographical latitude coordinate of the event's location.Longitude: The geographical longitude coordinate of the event's location.Date (Gregorian Calendar): The date when the event occurred, according to the Gregorian calendar.Hour (local): The local time when the event occurred.Hour (UTC): The time of the event in Coordinated Universal Time (UTC).Sound: A binary indicator (usually 1 for 'Yes' and 0 for 'No') showing whether there was a notable sound associated with the event.Hail: A binary indicator showing whether hail was a feature of the weather event.Electric Storm: A binary indicator showing whether the event involved an electric storm.Damage: A binary indicator showing whether there was any damage resulting from the event.Tornado: A binary indicator showing whether a tornado was a part of the event.Waterspout: A binary indicator showing whether a waterspout was observed during the event.Register: This column refers to the existence of some witness account or visual material of a rotating column.Max. EF Rating: The maximum Enhanced Fujita Scale rating assigned to the tornado, indicating its intensity.Analyst: The name or initials of the person who analyzed or reported the event.Fatalities: The number of fatalities (deaths) caused by the event.Injured: The number of injuries reported due to the event.Link to Documents: References or links to documents where the event is described or recorded.Sources: The sources or references from where the information about the event is derived.Comments: Additional remarks or notes about the event, providing context or extra details.

A database of verified tornado tracks across Canada has been created covering the 30-year period from 1980 to 2009. The tornado data have undergone a number of quality control checks and represent the most current knowledge of past tornado events over the period. However, updates may be made to the database as new or more accurate information becomes available. The data have been converted to a geo-referenced mapping file that can be viewed and manipulated using GIS software.

A database of verified tornado occurrences across Canada has been created covering the 30-year period from 1980 to 2009. The data are stored in a Microsoft Excel spreadsheet, including fields for date, time, location, Fujita Rating (intensity), path information, fatalities, injuries, and damage costs. In cases where no data were available, values in the database have been left blank. The tornado data have undergone a number of quality control checks and represent the most current knowledge of past tornado events over the period. However, updates may be made to the database as new or more accurate information becomes available. The database has also been used to produce PNG images and an interactive KML file that can be viewed using Google Earth.

A violent tornado outbreak occurred on December 10-11, 2021 in the Midwest US. One of the tornadoes, known as the Quad-State tornado, tracked across four states and devastated the downtown area of Mayfield, KY, producing high-end EF-4 damage. The data here provides a series of wind speed and direction time histories of the Quad-State tornado for 44 damaged residential houses in Mayfield, KY, which can be useful for detailed forensic analysis of the residential building damage. The data was generated using a software that performs a treefall pattern analysis method, developed by the first author. In addition to the many structural damage, the tornado damaged a large number of trees in the Mayfield area. The fall direction of the damaged trees displayed a converging pattern, caused by a rotational wind flow, which is a typical indicator of a tornado. The converging treefall pattern then can be analyzed to characterize the tornadic flow and estimate the wind field (i.e., treefall pattern analysis method). The treefall pattern analysis method simulates a series of tornadoes using an idealized Rankine vortex model and generates a virtual treefall pattern, which is used to compare to the treefall pattern observed in the field and iterated until the "best-matching" pattern is found. In order to reduce the uncertainty in the estimates, the translational speed of the tornado was estimated based on tracking the motion of the vortex signature from the nearest NEXRAD radar, and the Radius of Maximum Wind (RMW) and decay exponent of the Rankine vortex model were estimated based on the structural damage. Then, the software was used to estimate the rest of the vortex parameters and wind time history (e.g., wind speed and direction) at selected locations. More detailed description on the parameter estimation and software will be published later in the NIST Technical Note.

The National Weather Service (NWS) Storm Prediction Center (SPC) routinely collects reports of severe weather and compiles them with public access from the database called SeverePlot (Hart and Janish 1999) with a Geographic Information System (GIS). The composite SVRGIS information is made available to the public primarily in .zip files of approximately 50MB size. The files located at the access point contain composite track information regarding tornados, large hail, and damaging winds for the period 1950-2006. Although available to all, the data provided may be of particular value to weather professionals and students of meteorological sciences. An instructional manual is provided on how to build and develop a basic severe weather report GIS database in ArcGis and is located at the technical documentation site contained in this metadata catalog.

The National Weather Service (NWS) Storm Prediction Center (SPC) routinely collects reports of severe weather and compiles them with public access from the database called SeverePlot (Hart and Janish 1999) with a Graphic Information System (GIS). The composite SVRGIS information is made available to the public primarily in .zip files of approximately 50MB size. The files located at the access point contain track information regarding known tornados during the period 1950 to 2006. Although available to all, the data provided may be of particular value to weather professionals and students of meteorological sciences. An instructional manual is provided on how to build and develop a basic severe weather report GIS database in ArcGis and is located at the technical documentation site contained in this metadata catalog.

National Risk Index Version: March 2023 (1.19.0)A Tornado is a narrow, violently rotating column of air that extends from the base of a thunderstorm to the ground and is visible only if it forms a condensation funnel made up of water droplets, dust and debris. Annualized frequency values for Tornadoes are in units of events per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

The core breeding range of Swainson's warbler (Limnothlypis swainsonii) overlaps a zone of exceptionally high tornado frequency in southeastern North America. The importance of tornadoes in creating breeding habitat for this globally rare warbler and other disturbance-dependent species has been largely overlooked. This paper estimates tornado frequency (1950–2021) and forest disturbance in the 240 counties and parishes in which breeding was documented from 1988 to 2014. The frequency of destructive tornadoes (EF1-EF5) varied 6-fold across the breeding range with a peak in the Gulf Coast states. Counties from east Texas to Alabama experienced the lowest median return interval of 5.4 years per 1000 km2, resulting in approximately 2477 ha of forest damage per 1000 km2 per century, based on current forestland cover. Tornadoes were significantly less frequent north and east of the core breeding range, with return intervals increasing to 9.1 years per 1000 km2 for breeding counties on the Atlantic coastal plain, 10.2 years per 1000 km2 in the Ozark Mountains, and 32.3 years per 1000 km2 in the Appalachian Mountains. Breeding counties within 150 km of the coastline from east Texas to North Carolina are also subjected to the highest frequency of hurricanes in the Western Hemisphere. Hurricanes often cause massive forest damage but archived meteorological and forestry data are insufficient to estimate the aggregate extent of forest disturbance in breeding counties. Nevertheless, the combined impact of tornadoes and hurricanes in the pre-Anthropogenic era was likely sufficient to produce a dynamic mosaic of early-successional forest crucial for the breeding ecology of Swainson's warbler. To ensure the long-term survival of this rare warbler, it is advisable to develop habitat management plans that incorporate remote sensing data on early-successional forest generated by catastrophic storms as well as anthropogenic activities. This dataset comprises a catalog of 1717 song recordings of male Swainson's warblers (Limnothlypis swainsonii) on breeding territories in the southeastern United States. Songs were recorded from 1988 to 2014. The spreadsheet includes song recording field number (GRG), state, county or parish, date, latitude, and longitude. Breeding territories were located in 240 counties and parishes, which served as the geographic template for storm data analysis. Geographic coordinates were plotted in Fig 1 of "Catastrophic storms, forest disturbance, and the natural history of Swainson's warbler" (doi.org/10.1002/ece3.11099). Questions or inquiries regarding the dataset can be directed to the author.
Methods Geolocation of territorial Swainson's warblers. From 1988 to 2014, I surveyed breeding populations in 15 states as part of a comprehensive study of the warbler’s natural history. These surveys targeted Swainson’s warbler and were not incidental components of broader community censuses. Territorial males were documented in 240 counties and parishes documented by song recordings. Surveys were conducted during the breeding period, which began on 22 April in the Gulf Coast states and ended on 30 June in the Appalachian Mountains. I surveyed a wide spectrum of forestland and shrubland habitats, broadly classified as “forest land” by the USDA on public and private land and along waterways. Most breeding territories of this monogamous species were located using playback of songs, utilizing a protocol that was field-tested and fine-tuned in the late 1980s on the breeding and wintering ranges. Territorial males respond to playback by approaching the song source and delivering agitated “chip” notes, but usually refrain from singing until the playback source retreats or playback ends. Response to playback, mate-guarding, persistence during “playback-and-follow” trials, and counter-singing with other males were regarded as evidence of territoriality. Mist-netting or other handling was not required to document territoriality. The geographic coordinates of territories were recorded on site with Garmin™ GPS receivers (post-1998) or with Google Earth Pro from field notes and maps. All fieldwork was performed by the author.

These data include all tropical cyclone tornado reports used in Paredes et al. (2021) plus an additional year (e.g., 2020). These data will not be updated regularly. For the latest version, users should refer to https://www.spc.noaa.gov/misc/edwards/TCTOR/ or contact roger.edwards@noaa.gov.

Each specific tropical cyclone tornado record has been extracted from the broader Storm Prediction Center tornado database, for all Atlantic and Gulf of Mexico tropical cyclones to affect the continental United States from 1995–2020. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. Unlike other climatologies previously published in the literature, the chosen time period for this examination essentially covers only the full national deployment of the WSR-88D radar network in the United States. This permits consistent comparisons of a very large sample size of tropical cyclone tornado events (>1600) during the era of modernized National Weather Service warning and verification practices.

These are the requisite datasets, customized functions, and scripts associated with the paper: "Using neural network models and synoptic circulation patterns to project future changes in US tornado activity" by Cameron Lee, Omon Obarein, and Erik Tyler Smith (currently in preparation as of 21-Jan-2025). All datasets, custom functions, and scripts are in Matlab-based file formats (.mat for data, and .m for functions and scripts). The dataset named "Outputs_Xkeepsave_SOMxANN_v35.mat" is a Matlab 'cell-array' data file of the collection of final datasets for producing various graphics and tables in the above-mentioned manuscript. For any questions on the use of these datasets, custom functions and scripts, please contact Dr. Cameron C. Lee at Kent State University.

This research was funded by EPRI (Contract ID: 10016806; PI: Cameron C. Lee).

Spatial model of Vermont tornado climatology. Models Vermont tornado events per long-term data collection (data date-range is January 1950 - February 2019). Provides access to Vermont tornado-event information.Data-source credit: NCEI (National Centers for Environmental Information) (https://www.ncei.noaa.gov/).Downloaded tornado-event data--in CSV format--from NCEI database on 06/06/2019. Data period is 01/1950-02/2019. Imported data to a geodatabase. Used beginning latitude/longitude values to spatially enable the data; 1 record was missing a beginning latitude/longitude (record w/ EVENT_ID = 10355004)--estimated beginning latitude/longitude of that event by referencing its EVENT_NARRATIVE. Removed fields so that fields focus on core event-info. Projected data to Vermont State Plane NAD83 meters. Moved narrative fields (EVENT_NARRATIVE and EPISODE_NARRATIVE) fields to a separate non-spatial table; those fields have lengthy contents that exceed the shapefile text-field limit--intention is to make them available in open-data portal as CSV table that is joinable to the feature class (via EVENT_ID field).Feature-Class Climate_VTTORNADOS_point FIELD DESCRIPTIONS:EVENT_ID: Unique ID assigned by NWS to note a single, small part that goes into a specific storm episode.BEGIN_DATE: Beginning date.TOR_F_SCALE: Enhanced Fujita Scale describes the strength of the tornado based on the amount and type of damage caused by the tornado. The F-scale of damage will vary in the destruction area; therefore, the highest value of the F-scale is recorded for each event.DEATHS_DIRECT: The number of deaths directly related to the weather event.INJURIES_DIRECT: The number of injuries directly related to the weather event.DAMAGE_PROPERTY_NUM: The estimated amount of damage to property incurred by the weather event. (e.g. 10.00K = $10,000; 10.00M = $10,000,000)DAMAGE_CROPS_NUM: The estimated amount of damage to crops incurred by the weather event. (e.g. 10.00K = $10,000; 10.00M = $10,000,000)TOR_LENGTH: Length of the tornado or tornado segment while on the ground (minimal of tenths of miles)TOR_WIDTH: Width of the tornado or tornado segment while on the ground (in feet)ENDING_LAT: Ending latitude (not available in all records).ENDING_LON: Ending longitude (not available in all records).Table Table_VTTORNADOS_Narratives FIELD DESCRIPTIONS:EVENT_ID: Unique ID assigned by NWS to note a single, small part that goes into a specific storm episode. Can join to EVENT_ID field of Climate_VTTORNADOS_point.EVENT_NARRATIVE: The event narrative provides more specific details of the individual event. The event narrative is provided by NWS.EPISODE_NARRATIVE: The episode narrative depicting the general nature and overall activity of the episode. The narrative is created by NWS. Ex: A strong upper level system over the southern Rockies lifted northeast across the plains causing an intense surface low pressure system and attendant warm front to lift into Nebraska.VCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.

The NCDP U.S. Natural Hazards Climate Change Projections project provides an interactive map-based tool, with a downloadable dataset, to explore county-level future natural hazard projections for Wildfires, Tropical Cyclones (Hurricanes), Tornadoes, and Sea Level Rise under one or more climate change scenarios in the United States. This tool provides a new dimension of hazard data to supplement NCDPs Natural Hazards Index v2.0. This new tool is a collaborative effort between multiple academic and public institutions that bring together the most up-to-date science to anticipate future hazards to visualize mid- and end-century hazard indicator estimates under one or more climate change scenarios (i.e., Shared Socioeconomic Pathways 2 (SSP2) and 5 (SSP5)) or RCP 8.5) allowing users to compare each time period and scenario to a historical baseline and to see the anticipated direction and magnitude of change for each hazard. The product documentation provides source data links, individual dataset corresponding authors, and details on the use, limitations, and interpretations. All datasets included in this map application have their limitations. As such, interpretation of the data should be only one piece of information to determine risk, which may vary depending on local and regional mitigation and adaptation actions.

Cloud computing enables users to create virtual computers, each one with the optimal configuration of hardware and software for a job. The number of virtual computers can be increased to process large data sets or reduce processing time. Large scale scientific applications of the cloud, in many cases, are still in development.

For example, in the event of an environmental crisis, such as the Deepwater Horizon oil spill, tornadoes, Mississippi River flooding, or a hurricane, up to date information is one of the most important commodities for decision makers. The volume of remote sensing data that is needed to be processed to accurately retrieve ocean properties from satellite measurements can easily exceed a terabyte, even for a small region such as the Mississippi Sound. Often, with current infrastructure, the time required to download, process and analyze the large volumes of remote sensing data, limits data processing capabilities to provide timely information to emergency responders. The use of a cloud computing platform, like NASA’s Nebula, can help eliminate those barriers.

NASA Nebula was developed as an open-source cloud computing platform to provide an easily quantifiable and improved alternative to building additional expensive data centers and to provide an easier way for NASA scientists and researchers to share large, complex data sets with external partners and the public. Nebula was designed as an Infrastructure-as-a-Service (IaaS) implementation that provided scalable computing and storage for science data and Web-based applications. Nebula IaaS allowed users to unilaterally provision, manage, and decommission computing capabilities (virtual machine instances, storage, etc.) on an as-needed basis through a Web interface or a set of command-line tools.

This project demonstrated a novel way to conduct large scale scientific data processing utilizing NASA’s cloud computer, Nebula. Remote sensing data from the Deepwater Horizon oil spill site was analyzed to assess changes in concentration of suspended sediments in the area surrounding the spill site.

Software for processing time series of satellite remote sensing data was packaged together with a computer code that uses web services to download the data sets from a NASA data archive and distribution system. The new application package was able to be quickly deployed on a cloud computing platform when, and only for as long as, processing of the time series data is required to support emergency response. Fast network connection between the cloud system and the data archive enabled remote processing of the satellite data without the need for downloading the input data to a local computer system: only the output data products are transferred for further analysis.

NASA was a pioneer in cloud computing by having established its own private cloud computing data center called Nebula in 2009 at the Ames Research Center (Ames). Nebula provided high-capacity computing and data storage services to NASA Centers, Mission Directorates, and external customers. In 2012, NASA shut down Nebula based on the results of a 5-month test that benchmarked Nebula’s capabilities against those of Amazon and Microsoft. The test found that public clouds were more reliable and cost effective and offered much greater computing capacity and better IT support services than Nebula.