The following is a list of GOES-R Series meteorological, solar, and space weather data products that are available to the user community. For additional information about these products, click the links below. For sample datasets, please visit the sample data page. Fact sheets are available for for several data products, as well as information about the Algorithm Working Group.
The latest GOES-R product quality, validation, and operational status information can be found on the National Oceanic and Atmospheric Administration (NOAA) Satellite Information System (NOAASIS) website. GOES-16 product validation information is available on the NOAASIS GOES-16 Peer/Stakeholder Product Validation Reviews webpage. GOES-17 product validation information can be found on the NOAASIS GOES-17 Peer/Stakeholder Product Validation Reviews webpage.
Access GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) data through NOAA’s National Centers for Environmental Information terrestrial data access webpage.
Access GOES-R Series Extreme Ultraviolet and X-ray Irradiance Sensors (EXIS), Magnetometer, Space Environment In-Situ Suite (SEISS), and Solar Ultraviolet Imager (SUVI) data through NOAA’s National Centers for Environmental Information space weather data access webpage.
GOES-R Data Product Links
Advanced Baseline Imager (ABI)
Geostationary Lightning Mapper (GLM)
Space Environment In-Situ Suite (SEISS)
Magnetometer (MAG)
Extreme Ultraviolet and X-ray Irradiance Sensors (EXIS)
Solar Ultraviolet Imager (SUVI)
Advanced Baseline Imager (ABI) Product Descriptions
Aerosol Detection (Including Smoke and Dust)
The aerosol detection product will use several spectral bands made available on the GOES-R Series imager. The algorithm will use known spectral absorption and scattering properties of different aerosols to detect their presence in the atmosphere. The aerosol detection product enables forecasters to better monitor areas of smoke and dust, which can be critical factors in visibility and air quality forecasts. In addition to short-term prediction, this product also enables better monitoring of the long-term trends in aerosol quantities and distribution throughout the atmosphere.
GOES-16 aerosol detection product from April 27, 2017.
Aerosol Optical Depth (AOD)
The aerosol optical depth (AOD) product utilizes several spectral wavelengths of the ABI (Advanced Baseline Imager) to measure the reflectance properties of cloud-free pixels at the top of the atmosphere (TOA). These reflectance properties at the TOA are then fed into aerosol models to compute the surface reflectance and aerosol properties at the surface. The information provided by the AOD algorithm aids meteorologists and others in making critical air quality, visibility, and aviation forecasts. In addition, AOD product provides valuable data for climate models and helps climate scientists monitor and predict climate change.
GOES-16 aerosol optical depth product, February 12, 2017.
Aerosol Particle Size
The aerosol particle size product will be derived for every clear pixel using the retrieved aerosol optical depth product and two pairs of ABI spectral bands in the visible and near-infrared spectrum. By comparing the reflectances of the different wavelengths, various aerosol properties including size can be calculated. The Ångström exponent, which describes the wavelength dependence of aerosol optical depth, is used as a proxy for aerosol particle size. Larger values of the Ångström exponent indicate smaller size particles and vice versa.
Example comparison of the retrieved Ångström exponent (proxy for particle size) using the ABI (green) and MODIS (pink) algorithms with ground-based AERONET observations over land (left) and over ocean (right) for the years 2000-2009.
Clear Sky Masks
The clear sky mask algorithm takes advantage of the high spatial and temporal resolution of the GOES-R ABI visible, near-infrared, and infrared bands to automatically produce a cloud classification for each pixel: cloudy, probably cloudy, clear, or probably clear. This information is used extensively by downstream level-2 product algorithms that require the state of cloudiness in each pixel. Products such as land surface temperature (LST) and sea surface temperature (SST), for example, can only be reliably computed for pixels that are totally cloud free. The clear sky mask product can be used by the numerical weather prediction (NWP) community to identify which ABI pixel information should be assimilated for use in NWP models.
GOES-16 clear sky mask product. Four-level mask = (0) confidently clear, (1) probably clear, (2) probably cloudy, (3) confidently cloudy.
Cloud Layers/Heights
The cloud layers product is derived from the retrieved cloud top pressure product. Based on the value of the retrieved cloud-top pressure, each pixel is determined to contain either a high (above 440 hPa), middle (440 – 680 hPa), or low level (below 680 hPa) cloud. Forecasters will be able to use this information to determine areas of cloud growth and likelihood of precipitation.
Example of the cloud layers/height product as generated by the ABI cloud layer/height algorithm using Meteosat-8/SEVIRI data on January 7, 2006, at 12:00 UTC.
Cloud and Moisture Imagery
The cloud and moisture imagery product will utilize all 16 spectral bands of the GOES-R ABI to monitor the Earth, atmosphere, and ocean system. The measured reflectance (radiance) within the visible (infrared) bands are converted into brightness values (BVs) and brightness temperatures (BTs), respectively. The BVs and BTs are used to generate an array of products aiding forecasters in monitoring and predicting all kinds of hazards: weather, oceanographic, and climate-related phenomena.
GOES-16 Advanced Baseline Imager (ABI) imagery for each of the instrument’s 16 bands on December 18, 2017.
Cloud Optical Depth
Cloud optical depth uses both the visible and the near-infrared ABI bands during the daytime and a combination of infrared bands for nighttime detection. This product, together with the cloud particle size distribution product, provides valuable information about the radiative properties of clouds. These two properties enhance climate prediction, as they provide global climate models with higher quality data regarding the Earth’s energy and radiation budget.
GOES-16 cloud optical depth product, January 24, 2018.
Cloud Particle Size Distribution
The cloud effective particle size is computed using the same algorithm that estimates cloud optical depth (COD). Using both the visible and near-infrared ABI bands during the day and the infrared bands during the night, the GOES-R cloud optical and microphysical properties algorithm retrieves, simultaneously with COD, the cloud particle size. Cloud particle size provides valuable information about the radiative properties of clouds. This information combined with the information provided by the COD product provides very accurate information about the Earth’s radiation budget, yielding more accurate climate prediction possibilities.
GOES-16 cloud particle size distribution product, April 9, 2017.
Cloud Top Height
The cloud top height algorithm uses ABI infrared bands to simultaneously retrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for generating other downstream products that include cloud layer, cloud optical/microphysical products, and derived motion winds. Forecasters can use this information to determine areas of cloud growth and likelihood of precipitation. Other operational applications of this product include its use in aviation Terminal Aerodrome Forecasts (TAFs), supplementing upper-level cloud information to the ground-based Automated Surface Observing System (ASOS), and initialization of clouds in numerical weather prediction models.
GOES-16 cloud top height (feet) product, February 13, 2018.
Cloud Top Phase
The cloud type algorithm uses four GOES-R ABI infrared spectral bands to determine four different cloud phases: warm (>0C) liquid water, supercooled liquid water, mixed, and ice. The cloud phase product is a prerequisite for generating other downstream products that include cloud height, cloud optical properties, fog detection/depth, and aircraft icing. The cloud top phase product enables meteorologists to better monitor and track changes in the water properties of clouds, improve icing forecasts for the aviation community, and aid in improving warnings for severe weather. Cloud phase product information can also be used in advanced ABI applications such as severe weather prediction and tropical cyclone intensity estimation.
GOES-16 cloud top phase product, January 12, 2018.
Cloud Top Pressure
The cloud top height algorithm uses ABI infrared bands to simultaneously retrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for generating other downstream products that include cloud layer, cloud optical/microphysical products, and derived motion winds. Forecasters can use this information to determine areas of cloud growth and likelihood of precipitation. Other operational applications of this product include its use in aviation Terminal Aerodrome Forecasts (TAFs), supplementing upper-level cloud information to the ground-based Automated Surface Observing System (ASOS), and initialization of clouds in numerical weather prediction models.
GOES-16 cloud top pressure (hPa) product, February 13, 2018.
Cloud Top Temperature
The cloud top height algorithm uses ABI infrared bands to simultaneously retrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for generating other downstream products that include cloud layer, cloud optical/microphysical products, and derived motion winds. Forecasters can use this information to determine areas of cloud growth and likelihood of precipitation. Other operational applications of this product include its use in aviation Terminal Aerodrome Forecasts (TAFs), supplementing upper-level cloud information to the ground-based Automated Surface Observing System (ASOS), and initialization of clouds in numerical weather prediction models.
GOES-16 cloud top temperature (deg. C) product, February 13, 2018.
Derived Motion Winds
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
GOES-16 derived motion winds product using ABI Band 14 on November 23, 2017. High-level (100-400 hPa) winds are shown in violet; mid-level (400-700 hPa) are shown in cyan; and low-level (below 700 hPa) are shown in yellow.
Derived Stability Indices
The derived stability indices such as convective available potential energy (CAPE), lifted index (LI), total totals (TT), Showalter index (SI), and the K-index (KI) are computed from the retrieved atmospheric moisture and temperature profiles. These indices aid forecasters in nowcasting severe weather by providing them with a plan view of these atmospheric stability parameters. Forecasters use this information to monitor rapid changes in atmospheric stability over time at various geographic locations, thus improving their situational awareness in pre-convective environments for potential watch/warning scenarios.
GOES-16 derived stability indices product from July 1, 2017, including lifted index (upper left), convective available potential energy (upper middle), total totals (upper right), K-index (lower left), and Showalter index (lower middle).
Downward Shortwave Radiation: Surface
The downward shortwave radiation (DSR) product is an estimate of the total amount of shortwave radiation (both direct and diffuse) that reaches the Earth’s surface. The product algorithm uses spectral channels in both the visible and the infrared in addition to data regarding albedo and atmospheric composition to compute the downward shortwave radiation at the Earth’s surface. DSR has many applications both in the general and applied sciences. As one of the components of the surface energy budget, it is necessary for climate studies. Used together with cloud and aerosol properties it provides estimates of cloud and aerosol effects (forcing). It is also used in surface energy budget models, land surface assimilation models such as those used at NOAA NCEP, NASA LDAS, and ocean assimilation models either as an input (providing observationally-based forcing term), or as an independent data source to evaluate model performance. DSR data are also employed in estimating heat flux components over the coastal ocean to drive ocean circulation models. In agriculture, DSR is used as input in crop modeling. In hydrology, it is used in watershed and run-off analysis, which is important for determining flood risks and dam monitoring. The solar energy industry also needs estimates of DSR for both real-time and short-term forecasts for building energy usage modeling and optimization. Since high irradiance values result in surface drying, DSR is also used in monitoring fire risk.
GOES-16 downward shortwave radiation product, April 30, 2018.
Fire/Hot Spot Characterization
The fire/hot spot characterization product makes use of both visible and infrared ABI spectral bands to locate fires and retrieve sub-pixel fire characteristics. The product greatly improves upon the previous fire detection product by taking advantage of the higher spatial and temporal resolution available with the GOES-R Series ABI. Forecasters use this product to monitor wildfires, and more importantly, rapid changes in individual fires. Forecasters use this product as part of an arsenal of forecasting tools aimed at helping firefighting efforts.
GOES-16 active fire product from September 3, 2017. Fire pixels are shown in red.
GOES-16 Fire Mask product.
Land Surface Albedo
Land surface albedo (LSA) is defined as the ratio between outgoing and incoming irradiance at the earth surface. The LSA is a shortwave broadband blue-sky albedo over wavelengths between 0.4 and 3.0 µm. As the key component of surface energy budget, LSA can be used to drive/calibrate/validate climatic, mesoscale atmospheric, hydrological, and land surface models. Variation of LSA is also an important indicator of land cover and land use change. Analysis of long-term reliable albedo products will help better understand the human dimension of climate change and how the vegetation-albedo-climate feedbacks work. LSA is one of the Essential Climate Variables (ECVs) by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO). The frequent temporal refresh rate, fine spectral resolution, and large spatial coverage make the Advanced Baseline Imager (ABI) a unique data source for mapping LSA.
GOES-16 full disk land surface albedo on Feb. 21, 2020
Land Surface Bidirectional Reflectance Factor
The land surface bidirectional reflectance factor (BRF), also referred to as surface reflectance (SR), is a ratio between outgoing radiance at one given direction and incoming radiance at another given direction (same or different from the incoming direction). BRF is produced at the following wavelengths: 0.47 µm, 0.64 µm, 0.86 µm, 1.61 µm, and 2.26 µm. The land surface bidirectional reflectance factor is a byproduct of the land surface albedo algorithm. BRF is used to create the GOES-R fractional snow cover product and for vegetation monitoring.
GOES-16 full disk reflectance for ABI band 1 on Feb. 21, 2020.
Land Surface Temperature
The land surface temperature (LST) product is derived from GOES-R ABI longwave infrared spectral channels and is expected to be used in a number of applications in hydrology, meteorology, and climatology. Forecasters use it to forecast the occurrence of fog and frost. The land surface product is of fundamental importance to the net radiation budget at the Earth’s surface and to monitoring the state of crops and vegetation. It is an important indicator of both the greenhouse effect and the energy flux between the atmosphere and ground. Furthermore, it can be assimilated into climate, atmospheric, and land surface models to estimate sensible heat flux and latent heat flux.
GOES-16 land surface temperature product, February 20, 2018.
Legacy Vertical Moisture Profile
The legacy vertical moisture product estimates levels of moisture throughout the troposphere, providing a vertical profile of moisture. This product is computed simultaneously with the vertical temperature profile, thus providing a thermodynamic vertical profile of the atmosphere. Knowledge of the vertical distribution of atmospheric moisture is critical to forecasting severe weather. This vertical moisture information also serves to initialize the moisture field in regional and mesoscale numerical weather prediction models.
GOES-16 legacy vertical moisture profile product, July 1, 2017.
Legacy Vertical Temperature Profile
The legacy vertical temperature profile product estimates levels of temperature throughout the troposphere. This product is a continuation of the operational sounder product available on the previous GOES satellites. This product is used by National Weather Service field forecasters and in numerical weather prediction models, providing information regarding the vertical temperature structure of the atmosphere. The vertical temperature structure information provided by this product is important for severe weather prediction as it is used to compute a number of atmospheric stability parameters which provide guidance to weather forecasters on the stability of the atmosphere.
GOES-16 legacy vertical temperature profile product, July 1, 2017.
Radiances
GOES-R Series satellites measure radiances in 16 visible, near-infrared, and infrared spectral bands at high spatial and temporal resolutions. These radiances are used to identify cloudy and cloud-free regions within the satellites’ field of view. Data provided by the 16 spectral channels are used to generate many GOES-R products, and are also used in numerical weather prediction models, aiding meteorologists and others in monitoring and predicting all kinds of weather and other phenomena.
Rainfall Rate / Quantitative Precipitation Estimation
The ABI rainfall rate algorithm generates the baseline rainfall rate product from ABI infrared brightness temperatures and is calibrated in real time against microwave-derived rain rates to enhance accuracy. The algorithm generates estimates of the instantaneous rainfall rate at each ABI IR pixel. The information provided by the quantitative precipitation estimation is used by forecasters and hydrologists in flood forecasting. Much of the flooding that occurs is related to some form of convective weather. The higher spatial and temporal resolution available on the GOES-R Series ABI is able to automatically resolve rainfall rates on a much finer scale, enabling weather forecasters to produce more timely and accurate flood advisories and warnings.
GOES-16 rainfall rate product, March 23, 2018.
Reflected Shortwave Radiation
The reflected shortwave radiation product measures the total amount of shortwave radiation that exits the Earth through the top of the atmosphere. The algorithm uses several spectral channels in both the visible and infrared spectrum to measure the reflected shortwave radiation. Information from this product provides an integral piece of the Earth’s radiation budget, aiding in climate modeling and prediction.
GOES-16 reflected shortwave radiation product, April 30, 2018.
Sea and Lake Ice Age
The ice thickness and age products will be produced for each pixel observed by the GOES-R ABI and covered with ice. There are no direct ABI channels related to the algorithm which actually relies on some other retrieved products from ABI and parameterization schemes such as cloud mask and ice surface temperature that would use some or all ABI channels for their retrievals. The ice thickness and age algorithm uses a one-dimensional thermodynamic ice model (OTIM) which is based on the surface energy balance at thermo-equilibrium and contains all components of the surface energy budget to estimate sea and lake ice thickness up to three meters. An estimate of the ice age is then based on the retrieval of ice thickness. The sea and lake ice age product will help climate forecasters monitor short and long term changes in sea and lake ice.
Example of the ice thickness (left) and ice age (right) products over the Great Lakes as generated by the GOES-R ice age and thickness algorithm using MODIS Aqua data on February 24, 2008.
Sea and Lake Ice Concentration
The ice cover and concentration algorithm is responsible for the determination of all ABI pixels covered with ice, and estimation of ice concentration under clear conditions. Ice cover identifies if an ABI pixel is covered by ice and ice concentration reports the ratio of water surface covered by ice as a fraction (in tenths) of the area being considered. Total concentration includes all ice types that are present. The result of the ice cover algorithm is an ice "mask." Sea and lake ice influences the surface radiation budget, and affects the exchange of energy and moisture between the atmosphere and the underlying water. It is one of the key factors to consider in the atmospheric circulation, numerical weather forecasting, and climate models. Ice cover is also important for planning commercial transport. Ice cover and concentration are among the most important indices to study climate change. Accurate retrievals of ice cover and concentration are of high importance both to the scientific communities and to the public.
Example of the ice concentration product (%) over the Great Lakes as generated by the GOES-R sea and lake ice concentration algorithm using MODIS Aqua data on February 24, 2008.
Sea and Lake Ice Motion
The GOES-R Series ABI tracks the location of sea and lake ice against time, estimating the motion of sea and lake ice pixels for all clear and non-land ABI pixels. The sea and lake ice motion product will aid the shipping industry by providing valuable information on motion of potentially damaging sea and lake ice.
Example of the ice motion vectors derived from MODIS data acquired at Tromso, Norway, on May 8, 2008. The ice motion vectors are overlaid on composite 11µm MODIS imagery.
Sea Surface Temperature
The GOES-R Series provides forecasters with a sea surface temperature (SST) for each cloud-free pixel over water identified by the ABI. The SST algorithm employed on the GOES-R Series uses hybrid physical-regression retrieval in order to produce a more accurate product. Knowledge of SST can be beneficial for a large spectrum of operational applications that include: climate monitoring/forecasting, seasonal forecasting, operational weather and ocean forecasting, military and defense operations, validating or forcing ocean and atmospheric models, sea turtle tracking, coral bleach warnings and assessment, tourism, and commercial fisheries management.
GOES-16 sea surface temperature product, February 26, 2018.
Snow Cover
The fractional snow cover algorithm uses GOES-R ABI spectral information in the visible and near-visible portion of the energy spectrum to retrieve sub-pixel fractional snow cover and grain size estimates via computationally efficient spectral mixture modeling. This product supports a number of operational applications that include: assimilation into NOAA’s NOHRC snow model, as well as hydrologic forecasts and warnings, including river and flood forecasts, water management, snowpack monitoring and analysis, and climate studies.
Example of the snow cover product (right figure) as generated by the GOES-R snow cover algorithm using MODIS data (left image) over the Colorado Rockies on April 30, 2017.
Total Precipitable Water
The total precipitable water (TPW) product is computed from the retrieved atmospheric moisture profiles and represents the total integrated moisture in the atmospheric column from the surface to the top of the atmosphere. This product provides useful information to weather forecasters and hydrologists to improve their situational awareness for a number of situations that require forecasting of events, such as heavy rain, flash flooding, onset of Gulf of America return flow, and the onset of the Southwest United States monsoon. The TPW product also serves to initialize the moisture field used in numerical weather prediction models.
GOES-16 total precipitable water product, July 1, 2017.
Volcanic Ash Detection and Height
The volcanic ash product algorithm utilizes five GOES-R ABI infrared channels to automatically determine the height and mass loading properties of any pixel found to contain volcanic ash. Forecasters can use the volcanic ash product to identify areas where volcanic ash is present and potentially hazardous, and ultimately, issue more accurate aviation, air quality, and public health warnings. The volcanic ash product is useful for initializing dispersion models and volcanic ash trajectory prediction models. The more accurate mass loading detection may also aid in forecasting short-term climate changes due to volcanic eruptions.
GOES-16 false color imagery (upper left), ash confidence (upper right), ash/dust cloud height (lower left), and ash/dust loading (lower right), from February 2, 2018.
Geostationary Lightning Mapper (GLM) Product Descriptions
Lightning Detection (Events, Groups, and Flashes)
The GOES-R Series Geostationary Lightning Mapper (GLM) detects the light emitted by lightning at the tops of clouds day and night and collects information such as the frequency, location and extent of lightning discharges. The instrument measures total lightning, both in-cloud and cloud-to-ground, to aid in forecasting developing severe storms and a wide range of high-impact environmental phenomena including hailstorms, microburst winds, tornadoes, hurricanes, flash floods, snowstorms and fires.
GOES-16 GLM lightning detection product from December 28, 2017.
Space Environment In-Situ Suite (SEISS) Product Descriptions
Energetic Heavy Ions
The GOES-R Series Space Environment In-Situ Suite (SEISS) includes an energetic heavy ion sensor. The energetic heavy ion product measured by this sensor measures energetic heavy ion fluxes in the Earth’s magnetosphere. Information provided by this product is used in tandem with the magnetic energetic ion products, as well as the solar and galactic proton product to provide a comprehensive picture of the energetic particle environment surrounding the Earth. This information aids scientists in assessing the risk of radiation posed to astronauts and high-altitude aircraft. These ions cause many of the same issues as the solar and galactic protons, although the energy levels required for ions to have the same effect can be lower. This heavy ion product gives forecasters, operations personnel, and spacecraft designers critical data which will improve the capability to mitigate single event effects (SEEs), total ionizing dose (TID), and biological effects.
GOES-16 SEISS energetic heavy ion data from September 10-14, 2017.
Magnetospheric Electrons and Protons: Low Energy
The GOES-R Space Environment In-Situ Suite (SEISS) includes magnetic energetic ion sensors for both low and high energy levels. The low energy product gives the flux of low energy electrons and protons in the magnetosphere. The results can be used to determine the degree of spacecraft charging experienced by GOES-R. Spacecraft charging can cause arcing to occur between differentially charged surfaces on a spacecraft or to the surrounding plasma. These discharges can damage or destroy critical hardware. Numerous satellites have experienced anomalies or failure due to this effect.
Magnetospheric Electrons and Protons: Med and High Energy
The GOES-R Space Environment In-Situ Suite (SEISS) includes magnetic energetic ion sensors for both low and high energy levels. One of the space weather alerts issued by NOAA’s Space Weather Prediction Center (SWPC) will depend upon this product in the GOES-R Series era. These electrons are very penetrating, and can cause electrical breakdown and discharges of materials deep inside of equipment (deep dielectric charging). They can also adversely affect high frequency radio transmissions and therefore GPS navigation.
GOES-17 SEISS Magnetospheric Electrons and Protons: Medium and High Energy data May 4-7, 2018, showing the complex response of radiation belt electrons and protons associated with a geomagnetic storm. The large increase in the levels of electrons triggered a radiation belt alert by SWPC.
Solar and Galactic Protons
The GOES-R Series Space Environment In-Situ Suite (SEISS) includes a sensor that measures solar and galactic protons present within the Earth’s magnetosphere. This product will form the basis for Space Weather Prediction Center’s (SWPC’s) solar radiation storm warnings. Humans exposed to large fluxes of these particles (astronauts, passengers/crews on high flying planes) can suffer biological effects. The storms can also cause blackouts of high frequency (HF) radio communication near the poles. The resulting last-minute rerouting of transportation can have substantial economic impacts.
These particles also cause single event effects (SEEs) which can have either temporary or permanent effects on satellites and instruments. The total ionizing dose (TID) of these particles which equipment is subjected to also degrades performance and can reduce lifetime. Understanding the environment permits selection of parts and shielding to permit satellites to fulfill their mission.
SWPC’s current GOES proton product can be seen by visiting this link. In the GOES-R era this product can be created based upon results from the GOES-R solar and galactic protons product.
SWPC’s current GOES Proton Product. In the GOES-R era, this product can be created based upon results from the GOES-R Solar and Galactic Protons product.
Magnetometer (MAG) Product Descriptions
Geomagnetic Field
The Earth’s geomagnetic field surrounds the Earth and protects it from dangerous solar radiation. The geomagnetic field product monitors changes in the Earth’s geomagnetic field in three-dimensional space. These measurements are used to determine when magnetopause crossings occur, which is important sign of the arrival of some major space weather events, such as coronal mass ejections. Such massive events can cause geomagnetically induced currents (GIC) in power grids, resulting in power outages. Because the magnetic field is used by the attitude control system of some satellites, these crossings are a sign that those satellites could experience issues as well. This product helps detect geomagnetic storms and sub-storms, providing early warnings to power and communication services.
GOES-16 outboard Magnetometer data from December 22, 2016, shows a plasma wave.
Extreme Ultraviolet and X-ray Irradiance Sensors (EXIS) Product Descriptions
Solar Flux: EUV
The GOES-R Series Extreme Ultraviolet and X-ray Irradiance Sensors (EXIS) instrument includes a sensor that measures extreme ultraviolet (EUV) light from the sun. EUV radiation has major impacts on the ionosphere. Increased EUV radiation can result in radio blackouts of terrestrial high frequency (HF) communications. Increased EUV energy deposited in the Earth’s upper atmosphere (thermosphere) also results in increased atmospheric drag on satellites in low earth orbit (LEO). This EUV product provides improved measurements of these important wavelengths and information that assists operators of radio communication and navigation systems and satellites.
EXIS measures multiple x-ray and ultraviolet wavelengths. The specific wavelengths were chosen to monitor the different layers of the sun’s outer atmosphere and will be combined to create the full EUV spectrum of the sun every 30 seconds.
GOES-17 EXIS Extreme Ultraviolet Sensor measurements for the first 10 days of operation show that solar activity is slightly decreasing during this time period, with no major flares. The plot shows the Magnesium Index, a number that represents the ultraviolet variability of the sun. EXIS is able to measure changes in the Magnesium Index with better precision and much higher frequency than any previous satellite.
Solar Flux: X-Ray
The GOES-R Extreme Ultraviolet and X-Ray Irradiance Sensors (EXIS) measures light from the sun. The NOAA Space Weather Prediction Center (SWPC) relies on this product to issue warnings when there are large increases in solar X-ray output from solar flares. These X-ray flares cause changes in the ionosphere and are used by SWPC to give warnings of radio blackouts of terrestrial high frequency (HF) radio communications.
Solar Ultraviolet Imager (SUVI) Product Descriptions
Solar EUV Imagery
The GOES-R Series Solar Ultraviolet Imager (SUVI) solar EUV imagery products provide space weather scientists with images of the sun in several different EUV spectral bands. These high-resolution images reveal details about the structure of active regions, filaments, and solar prominences. Also of interest to space and solar weather scientists are the boundaries of coronal holes and how the entire surface of the sun behaves during solar flares. Higher-level products made from these imagery products by the NOAA Space Weather Prediction Center along with other organizations provide early warning of potential radiation hazards, such as SEP events, flares, geomagnetic storms and radio blackouts.
GOES-16 SUVI imagery of a large coronal hole on March 27, 2017, in six ultraviolet wavelengths: 171, 195, 284, 304, 94, and 131 angstrom.
Compact Coronagraph (CCOR) (GOES-U only) Product Descriptions
CCOR White Light Intensity (GOES-U Only)
The GOES-U Compact Coronagraph (CCOR) will image the solar corona (the outer layer of the sun’s atmosphere) and help detect and characterize coronal mass ejections (CMEs). CCOR’s primary data product is coronal white light intensity from which NOAA’s Space Weather Prediction Center (SWPC) can perform CME characterization. Higher-level data products include CME velocity, direction, and mass. SWPC will use these products to predict geomagnetic storm conditions at least one day in advance.
Large Angle and Spectrometric Coronagraph (LASCO) image of the upper solar corona, extending out to 13 million miles away from the sun, showing the eruption of a coronal mass ejection on February 27, 2000. The GOES-U CCOR data products will be similar.