GOES Quick Guides

GOES Single-Band Quick Guides

Band # Central Wavelength Nickname AWIPS Legend Guide Update
1 0.47 µm Blue Visible Band CH-01-0.47um Aug 2017
2 0.64 µm Red Visible Band CH-02-0.64um May 2017
3 0.86 µm Veggie Band CH-03-0.87um May 2017
4 1.37 µm Cirrus Band CH-04-1.38um Aug 2017
5 1.61 µm Snow/Ice Band CH-05-1.61um Aug 2017
6 2.24 µm Cloud Particle Size Band CH-06-2.25um Aug 2017
7 3.90 µm Shortwave Infrared CH-07-3.90um Sep 2017
8 6.19 µm Upper-Level Water Vapor CH-08-6.19um Aug 2017
9 6.9 µm Mid-Level Water Vapor CH-09-6.95um Aug 2017
10 7.34 µm Lower-Level Water Vapor CH-10-7.34um Sep 2017
11 8.5 µm IR Cloud Top Phase Band CH-11-8.50um May 2017
12 9.6 µm Ozone Band CH-12-9.61um Aug 2017
13 10.35 µm Clean Window IR CH-13-10.35um Aug 2017
14 11.2 µm Legacy Window IR CH-14-11.20um Aug 2017
15 12.3 µm Dirty Window IR CH-15-12.30um Aug 2017
16 13.3 µm CO2 Longwave CH-16-13.30um Sep 2017

 

GLM Quick Guides

Name Description
GLM and Ground-Based Networks              This is a quick guide highlighting the strengths and differences between the Geostationary Lightning Mapper and the ground-based lightning detection networks.
GLM Applications This provides an overview of the advantages, limitations, and potential uses of the Geostationary Lightning Mapper's flash extent density product.
GLM Detection Methods The GLM creates background images every 2.5 minutes, then images 500 frames per second to detect changes in brightness relative to the background image. Individual pixels that are illuminated above the background threshold during a 2 ms frame are termed GLM events, filters then determine the likelihood that these events are real lightning.
GLM Data Quality Issues described include geospatial considerations and false events.
GLM Full Disk Data Quality Performance variability, sources of false flashes, and additional data quality considerations when using full disk GLM products.
GLM Gridded Products/FED Flash extent density (FED), the number of flashes that occur within a grid cell over a given period of time, is the first NWS gridded GLM product.
GLM Gridded Products/AFA and TOE Average flash area (AFA) is the average area of all GLM flashes spatially coincident with each 2×2 km grid cell during a specified time period. Total optical energy (TOE) is the sum of all optical energy that the GLM observes within each grid cell during a specified time period.
GLM Minimum Flash Area (MFA) This provides an overview of Minimum Flash Area (MFA), which reports the minimum size of any GLM flash spatially coincident with each 2×2 km grid cell during a specified time period.  
GLM Full Disk Gridded Products This provides an overview of GLM Full Disk Gridded products and applications.

 

RGB Quick Guides

Name Red Green Blue Common Uses

Air Mass

 6.2 - 7.3 µm 9.6 - 10.3 µm 6.2 µm (inverted) Identifying air masses, inferring cyclogenesis
Day Snow Fog 0.86 µm 1.6 µm 3.9-10.3 µm The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud.
Day Convection 6.2 - 7.3 µm 3.9 - 10.3 µm 1.6 - 0.64 µm Identify intense vs. weak/mature updrafts
Day Land Cloud 1.6 µm 0.86 µm 0.64 µm Identify surface features (inc. fire hotspots), high vs. low clouds, snow
Day Land Cloud Fire 2.2 µm 0.86 µm 0.64 µm Identify surface features, high vs. low clouds, snow
Differential Water Vapor 7.3 - 6.2 µm (inverted) 7.3 µm (inverted) 6.2 µm (inverted) Analyze water vapor distribution, depth of moisture, trough/ridge patterns
Dust 12.3 - 10.3 µm 11.2 - 8.4 µm 10.3 µm Identifying dust
Fire Temperature 3.9 µm 2.2 µm 1.6 µm Assess more intense vs. less intense fires
Nighttime Microphysics 12.4 - 10.4 µm 10.3 - 3.9 µm 10.3 µm Identify high clouds vs. low clouds vs. fog at night
Simple Water Vapor 10.35 µm (inverted) 6.19 µm (inverted) 7.34 µm (inverted)

Assess distribution of moisture

CIMSS Natural True Color

 0.64 µm

0.45* (0.64 µm)

0.1*(0.86 µm)

0.45*(0.47 µm)

 0.47 µm

True color gives an image the approximate look as one would see from space.

Day Cloud Convection

 0.64 µm 0.64 µm 10.3 µm Helps to distinguish between high and low clouds and can help reveal wind shear when animated. 

Day Cloud Phase Distinction

 10.3 µm 0.64 µm 1.6 µm This RGB is used to evaluate the phase of cooling cloud tops to monitor convective initiation, storm growth, and decay.

Ash RGB

 12.3 - 10.3 µm 11.2 - 8.4 µm 10.3 µm The Ash RGB can be used both day and night for the detection of and monitoring of volcanic ash as well as sulfur dioxide gas. 

SO2 RGB

 6.95 - 7.34 µm 10.35-8.5 µm 10.3 µm The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well as industrial facilities such as power plants.
Day Cloud Type RGB 1.37 µm 0.64 µm 1.6 µm Differentiates cloud types. Detection and discrimination of thin and thick cirrus clouds.

 

Channel Difference Quick Guides

Name Channel Difference Common Uses
Night Fog Difference 10.3 - 3.9 µm

Identify clouds made up of small water droplets (e.g., fog/stratus)
Split Window Difference 10.3 - 12.3 µm Identify gradients in moisture, regions of low-level dust
Split Cloud Phase 8.5-11.2 µm Differentiate cloud size particles
Split Water Vapor Difference 6.2-7.3 µm The band difference give an approximation of the concentration and distribution of Water Vapor
Split Snow 1.6-0.64 µm Highlights regions where ice is present
Split Ozone 9.6-10.3 µm Reveals the influence of ozone absorption

 

Baseline Product Quick Guides

Name Common Uses
Cloud Phase Describes the cloud-top composition.
Legacy Vertical Profiles Legacy Vertical Profiles show GFS information that has been adjusted based on satellite information.
Clear Sky Mask Establishes the presence or lack of clouds (mainly used for other subsequent algorithms)
Cloud Top Height Estimates the top of the cloud in feet.
Aerosol Detection Identifies the type of aerosol in the atmosphere. The product is used to identify obstructions to visibility and for forecasts of air quality.
Cloud Top Pressure Baseline Cloud Top Pressure estimates the cloud top pressure (in hPa). Values are not assigned in clear skies.
Cloud Top Temperature The Baseline Cloud Top Height Temperature estimates the temperature of the cloud top in degrees Celsius; it is generally more accurate than individual channel Brightness Temperatures that can be affected by absorption by gases.
Cloud Optical Depth The GOES-R Cloud Optical Depth provides valuable information on the radiative properties of clouds.
Cloud Particle Size Distribution The GOES-R Cloud Particle Size Distribution is a fundamental product to determine liquid and ice water content of clouds.
Total Precipitable Water The TPW product is useful for following rapidly evolving events (i.e., convective) since it is available at high time resolution.
Aerosol Optical Depth Aerosol Optical Depth (AOD) is a quantitative estimate of the amount of aerosol present in the atmosphere, and it can be used as a proxy for surface Particulate Matter PM2.5
Derived Motion Winds Derived Motion Wind Vectors are produced using sequential ABI images. They can provide important information about winds at different levels during asynoptic times.
Derived Stability Indices Five different GOES-based stability indices are available: Total Totals, K, Showalter and Lifted Indices, and Convective Available Potential Energy (CAPE).
SST The SST product is useful for analyzing oceanic Sea Surface Temperatures
Fire/Hot Spot Characterization The GOES-R Fire/Hot Spot Characterization consists of Fire Area, Fire Power and Fire Temperature products. These products are used  to better monitor wildfires and their rapid changes by leveraging the higher spatial and temporal resolution of the GOES-R ABI. 
Volcanic Ash The Volcanic Ash algorithm determines the location, height and mass loading properties for satellite pixels potentially containing volcanic ash.

 

Enterprise Product Quick Guides

Name Common Uses
GOES IFR Probability

GOES-R IFR Probability fields combine cloud information from GOES- 16/GOES-17 and low-level saturation information from the Rapid Refresh model.
GOES Cloud Thickness

GOES-R Cloud Thickness Fields estimate the depth of the lowest deck of clouds made up of water droplets.
Turbulence Probability GOES-R Turbulence Probabilities use machine learning methods to relate satellite feature from clean window IR (10.3 µm) and water vapor imagery (6.19 µm)).