This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 m spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the ABoVE extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems.

This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques.

This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks.

The Landslide Hazard Analysis for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite‐based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a “nowcast” is issued to indicate the times and places where landslides are more probable. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run and spatial coverage is from 60°N to 60°S .

Version 4 is the current version of the data set. Older versions are no longer available and have been superseded by Version 4.

These data are a part of MEaSUREs 2012 projects. The particular project, "Multi-Decadal Sulfur Dioxide Climatology from Satellite Instruments", is expected to produce SO2 Earth Science Data Record by means of combining measurements from backscatter Ultraviolet (BUV), thermal infrared (IR) and microwave (MLS) instruments on multiple satellites. The data represent best estimates of the volcanic and anthropogenic contribution to global atmospheric SO2 concentrations. Since SO2 is the major precursor of sulfate aerosol, which has climate and air quality impact, SO2 measurements will contribute to better understanding of the sulfate aerosol distributions and its atmospheric impact."

The released data file is a long-term database of volcanic SO2 emission derived from ultraviolet satellite measurements from October 31, 1978, to present.

Data are in a table format in simple ASCII format:

Column Descriptions:
Column 1 = Name of volcano.
Column 2 = Latitude of volcano.
Column 3 = Longitude of volcano.
Column 4 = Altitude of volcano (km).
Column 5 = Eruption year.
Column 6 = Eruption month of year.
Column 7 = Eruption day of month.
Column 8 = Eruption style: exp = explosive, eff = effusive.
Column 9 = Eruption volcanic explosivity index (nd = no data or undetermined).
Column 10 = Observed plume altitude (km) where known.
Column 11 = Estimated plume altitude (km) above vent: 10 km for explosive, 5 km for effusive.
Column 12 = Measured SO2 mass in kilotons (= 1000 metric tons).

The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).

The World Wide Lightning Location Network (WWLLN) is a global, ground-based lightning sensor network operated by the University of Washington in Seattle. This network monitors and maps global lightning activity. WWLLN has generated quality controlled global lightning data for storms studied during the 2012-2014 Hurricane and Severe Storm Sentinel (HS3) campaign to track and analyze lightning activity.

The Hurricane and Severe Storm Sentinel (HS3) Global Hawk Navigation dataset consists of the real-time navigation and housekeeping data that was acquired from various instruments aboard the Global Hawk including the LN-100G IMU navigation system and the Global Hawk flight computer during the HS3 campaign. The goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes, and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. This dataset was broadcast on the Global Hawk aircraft network by the NASDAT (NASA Airborne Science Data Acquisition and Transmission unit) as 1 Hz Universal Datagram Protocol (UDP) packets. These UDP packets were generated in IWG1 format, a type of ASCII format supported by all NASA and NCAR aircraft.

The GRIP Hurricane and Tropical Storm Forecasts dataset consists of tropical cyclone model forecast tracks archived during the NASA Genesis and Rapid Intensification Processes (GRIP) field campaign. GRIP was one of three hurricane field campaigns conducted during the 2010 Atlantic/Pacific hurricane season. This tri-agency effort included NASA GRIP, the NSF Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) and the NOAA Intensity Forecasting Experiment 2010 (IFEX10). The hurricane and tropical storm forecasts data files are available from August 12 through November 14, 2010 in ASCII text format with browse files in KML format, viewable in Google Earth. The ASCII text files contain 5-day model “consensus” forecasts and the KML browse files contain model forecasts ranging from 5-days to 10-days.

The GPM Ground Validation Global Flood Monitoring System (GFMS) Flood Maps IFloodS dataset contains global flood estimates on a 0.25 degree spatial resolution every 3 hours, from March 26, 2013 through June 30, 2013. These data are provided in support of the Iowa Flood Studies (IFloodS) experiment conducted in eastern Iowa. The goals of the IFloodS campaign were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. The data are available in netCDF-4 and ASCII formats. Flood map and rain graph files are available in KMZ, JPG, and GIF formats.