README.md

This repository folder contains Data Set samples.

List of the Data Sets

Name		Description
NOAA global weather analysis		NOAA global weather analysis is all about weather analysis from the Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA).
ERA5 Global weather		As any reanalysis product, ERA5 combines observed data with the output of meteorological models. Note that there are actually two versions of ERA5 data. Initial data is referred to as ERA5T and available in near real time. I.e., ERA5T data lags real time by about three days. About three months later, the final version of the data is released. This is the actual ERA5 data. This Data Set contains both ERA5 and ERA5T data. With the latter being uploaded initially and overwritten once the former is available. As far as currently known, differences between the two versions are negligible. The Data Set contains data for 1980, 1990, 2000, 2005 and from 2009 onwards.
Sentinel 2 high resolution imagery		Sentinel-2 is a set of two satellites in polar orbit 180 degrees apart. It monitors land surface and coastal waters every 5 days at the equator and more frequently at mid-latitudes. The coverage is between latitudes 56° south and 84° north. Images are in 13 spectral bands at various ground resolutions: 4 bands at 10 m, 6 at 20 m and 3 at 60 m; the orbital swath is 290 km wide. Level 2A (L2A) images are 100x100 km ortho-rectified and spatially registered on a global reference system; they are corrected for the atmosphere so they represent ground conditions. Currently Geospatial APIs ingests Bands 4 (red), 8 (NIR) and SCL (Scene Classification). An NDVI layer, called "NDVI sh", is calculated from Bands 4 and 8. Tiles are ingested on request. Currently there is some coverage for tiles in USA, Brazil, India and the Netherlands for selected days in 2018 and 2019. Timestamps in this dataset are rounded down to 0:00 UTC from the Satellite's sensing time.
GFS latest 16 day forecast		Numerical weather prediction system that contains a global computer model and variational analysis from the United States National Weather Service (NWS).
GFS 16 day forecast		The Global Forecast System (GFS) is a global numerical weather prediction system containing a global computer model and variational analysis run by the United States National Weather Service (NWS). The mathematical model is run four times a day, and produces forecasts for up to 16 days in advance, but with decreased spatial resolution after 10 days. This Data Set currently contains forecasts from the daily 18:00 UTC run. Moreover, timestamps generally correspond to the valid time of the forecast; that is, the timestamp the forecast is for. All layers in this Data Set have a dimension called "horizon", indicating the difference between the issue and valid time in hours. Thus, one obtains a forecast for 0:00 UTC issued at 18:00 the day before by querying 0:00 with "horizon" 6.
NOAA daily global weather		The Global Data Assimilation System (GDAS) is a critical component of the National Center for Environmental Prediction's (NCEP) Global Forecast System (GFS). It integrates a wide variety of observational data into a gridded, three-dimensional model space to initialize weather forecasts. The system incorporates data from diverse sources, including surface observations, balloon measurements, wind profilers, aircraft reports, buoys, radar, and satellites. This assimilation process ensures that the GFS model starts with the most accurate and up-to-date information, enhancing the quality of weather predictions. GDAS data is generated four times a day and is provided at a spatial resolution of 0.25 degrees.
CMIP global climate models		The Coupled Model Intercomparison Project (CMIP6) protocol was endorsed in 2014 and consists of global climate simulations from around 100 distinct climate models being produced across 49 different modeling groups. CMIP6 represents a substantial expansion over CMIP5, in terms of the number of modeling groups participating, a new set of emissions scenarios driven by different socioeconomic assumptions have been developed called Shared Socioeconomic Pathways (SSPs) which drive the climate models for CMIP6.
NASA global elevation data		The datasets result from a collaborative effort by NASA and the National Geospatial-Intelligence Agency, as well as the participation of the German and Italian space agencies. Together, this international space collaboration generated a near-global digital elevation model of the Earth using radar interferometry. The 'targeted landmass' consisted of all land between 60° North and 56° South latitude, which comprises almost exactly 80% of the Earth’s total landmass. The coverage reached somewhat further north than south because the side-looking radar looked toward the north side of the Shuttle. NASA Version 3.0 SRTM (SRTM Plus) data includes topographic data from non-SRTM sources to fill the gaps (“voids”) in earlier versions of SRTM data.
Global statistical elevation data		The Global statistical elevation data primarily relies on the SRTM (Shuttle Radar Topography Mission) data as its core elevation source. However, gaps or voids in the SRTM data are filled using a variety of supplementary datasets, including non-SRTM DTED, Canadian Elevation Data (CDED), SPOT 5 Reference3D, National Elevation Dataset (NED) for the US and Alaska, GEODATA 9-second DEM for Australia, as well as DEMs from Antarctica and Greenland derived from satellite radar and laser altimetry. To integrate these various datasets effectively, the Delta Surface Fill (DSF) method, developed by the National Geospatial-Intelligence Agency (NGA), is employed. The DSF technique adjusts the fill data to align with the SRTM surface at the void interface, ensuring that the filled areas maintain the topographical trends of the original data while preserving valuable characteristics from the supplementary sources.
Global elevation data		The Global elevation data is sourced from the Japan Aerospace Exploration Agency (JAXA). This is an elevation data set that can express undulations of terrain over the world with a spatial resolution of 1 arcsecond (~30m at equator). The data set contains three layers as the actual elevation data, a quality band and a layer indicating any auxiliary data source that has been used to fill missing value. Elevation data that has a vertical accuracy of 5 meters within 1 standard deviation.
Landsat 8 high resolution imagery		This data set provides high resolution imagery from the Landsat 8 satellite from the National Aeronautics and Space Administration (NASA) that includes conditions at the top of the atmosphere.
USGS basic land elevation data		The basic land elevation data is sourced from the National Elevation Dataset (NED) of the United States Geological Survey (USGS).
PRISM daily US weather		The PRISM daily US weather dataset refers to daily climate data produced by the PRISM Climate Group at Oregon State University. PRISM (Parameter-elevation Regressions on Independent Slopes Model) is a climate mapping system that uses statistical techniques to incorporate spatial patterns like elevation, proximity to water, and terrain features to provide high-resolution climate data primarily for the United States.
Climatologies and validation data		The climatology was calculated by the IBM Future of Climate team from ERA5 data spanning 1991-2020. The methodology similar for that used in the ERA-interim climatology. See the linked references by Jung and Leutbecher as well as Janoušek. That is, a 61 day weighted rolling window with the weights decreasing linearly from their maximum value at the center of the window to zero at +-30 days. This is in contrast to those references (and the ERA-interim climatology), where weights are determined by second-order polynomial. The daily data is stored in Geospatial ranging between Jan 1 2020 to Dec 31 2020. This dataset is mainly used to compare current or forecasted weather data with historical climate benchmark and observe how much it varies with the historical norm.
Soil properties and soil profile data		Soil properties and soil profile data is the Data Set that consists of collections of soil property maps for the world produced using machine learning at 250 m resolution. Predictions are made at six standard depths. SoilGrids uses global models that make use of all available input point data to map a property across the globe. This results in consistent predictions (no abrupt changes in predicted values at country boundaries, etc).
Current and historical weather		This dataset provides high-resolution, hourly global weather data at a 4km grid resolution. It includes both current and historical weather data, covering landmasses and coastal waterways. The dataset spans from July 2015 to present, offering detailed meteorological information that is crucial for a range of applications including environmental monitoring, weather forecasting, and operational planning. The data is updated hourly (every 20 minutes past every hour).The dataset features a 4km grid resolution, covering both landmasses and coastal waterways for high accuracy in localized weather analysis. It offers hourly temporal resolution for up-to-date insights into evolving weather conditions and provides historical data from July 2015 to the present for long-term trend analysis. Special case data includes the Driving Difficulty Index, available from December 15, 2015, 17Z onward, which assesses weather-related challenges for transportation and road conditions. Additionally, the Pressure Mean Sea Level (MSL) and Wind Direction data are available starting from July 17, 2017, 15Z onward, providing high-accuracy atmospheric pressure and wind pattern information. This gridded dataset is ideal for industries and applications that require precise, high-frequency weather data, including climate research, logistics, agriculture, and environmental monitoring. The flexibility of on-demand access ensures that users can retrieve the data they need when they need it, with the ability to perform custom analysis or integrate with other systems.
Monthly seasonal forecasts		The TWC Seasonal PFP offering consists of 50 equally likely scenarios. Maximum, minimum and average temperature, as well as total precipitation forecasts, are offered, extending out to 6-months at daily resolution. Forecasts are produced at monthly intervals (corresponding to the valid times of the forecasts), spanning from 01-01-2015 to present. The underlying data source is from the ECMWF-S5 climate model, which is calibrated against the ERA5 dataset.This dataset is used to compare current or forecasted weather data with historical climate benchmark and observe how much it varies with the historical norm.
USDA soil properties		It contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. It is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The survey data was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories.
Spatial grids of fire danger indexes		The dead fuel moisture threshold (10–hour, 100–hour, or 1,000–hour), called a time lag, is based upon how long it would take for 2/3 of the dead fuel to respond to atmospheric moisture. The fuel moisture index is a tool that is widely used to understand the fire potential for locations across the country. Fuel moisture is a measure of the amount of water in a fuel (vegetation) available to a fire, and is expressed as a percent of the dry weight of that specific fuel. 10-hour fuel: This refers to vegetation with a diameter of 0.25 to 1 inch. Moisture content for this fuel type can be calculated using weather data at the time of observation, including temperature, humidity, and cloud cover. Alternatively, it can be an observed value from a standard set of fuel sticks, which are weighed as part of fire weather observations.100-hour fuel: This includes vegetation with a diameter between 1 and 3 inches. Its moisture content is typically calculated using the average weather conditions over the past 24 hours, such as day length, rain hours, and the daily range of temperature and humidity.1,000-hour fuel: This refers to vegetation with a diameter between 3 and 8 inches. Moisture content for this fuel type is computed from the average weather conditions over the past 7 days, including factors like day length, rain hours, and the daily range of temperature and humidity.
Wildfire risk potential		Wildfire risk potential provides calculation of wildfire hazard potential by the U.S. Department of Agriculture (USDA) and the United States Geological Survey (USGS) to inform evaluations of wildfire risk or prioritization of fuel management.