GINA

Student Exchange Forum 2026

Grace Veenstra — Tue, 07 Jul 2026 20:35:52 +0000

Student Exchange Forum: Coding and Geospatial Tools for Environmental Data Analysis

Come listen to undergradute interns from Geographic Information Network of Alaska (GINA) and Alaska Satellite Facility (ASF) and GLOBE* high school science club students from Ikuta High School in Kanagawa, Japan present their journeys on learning and using coding and geospatial tools for environmental data analysis.

Student Presenters:

Alex Lewandowski – From Intern to Software Engineer at ASF
Elliot Lewandowski – Earth Science and Computer Science – Synergies of parallel learning as an intern at ASF
Ikuta High School Science Club – Python-Based Analysis of GLOBE* Hydrosphere Data. Visiting students: Sota Inagaki, Rindo Tsuda, Shota Terashima, Kanako Hagiwara, Yuri Tamura, Takumi Karasawa, and Haruto Naikiyo
Briana Shaffer – Investigating NASA
SnowEX 2023 Data with Geographic Information Systems

*Global Learning and Observations to Benefit the Environment, a world-wide crowdscource science program supported by NASA

The Problem with Parallax: Fire Addendum

Grace Veenstra — Wed, 01 Jul 2026 02:04:35 +0000

As covered in the “Problems with Parallax” series, at high latitudes geostationary satellites can experience displacement of features and lower resolution due to parallax. In this addendum, we are comparing the NGFS fire product produced by ABI and VIIRS, which are found on the geostationary GOES-18 and the polar-orbiting NOAA-21 respectively.

This first set of images shows examples of the NGFS product taken by GOES-18 with ABI (left) and by NOAA-21 with VIIRS (right) on June 28, 2026 in the Northwest Territories of Canada. Notice the difference in the NGFS point locations produced using ABI versus VIIRS, as well as the difference in the underlying imagery.

Shortwave Infrared images from GOES-18’s ABI Ch07 (3.90 um) showing areas of high heat in red.

Shortwave Infrared images from NOAA-21’s VIIRS i04 (3.74 um) showing areas of high heat in red.

The GOES-18 ABI DayFire RGB, which shows fires in red; land cover in shades of green and brown; and smoke in grey-cyan.

The NOAA-21 VIIRS DayFire RGB, which shows fires in red; land cover in shades of green and brown; and smoke in grey-cyan.

In the DayFire RGB image comparison below, notice how ABI parallax changes things. The fires get stretched and deformed but because they are near the surface there is little actual displacement, however the elevated smoke is displaced 20-25 miles northward.

The GOES-18 ABI DayFire RGB, which shows fires in red, land cover in shades of green and brown, and smoke in grey-cyan.

The NOAA-21 VIIRS DayFire RGB, which shows fires in red, land cover in shades of green and brown, and smoke in grey-cyan.

So if there isn’t a lot of parallax displacement between ABI and VIIRS with fires at the surface, why are the ABI NGFS points so far away from the VIIRS NGFS points? The reason is pixel size. An NGFS point is located at the center point of a pixel and since ABI pixels are so large and stretched, the center points and resulting NGFS points are displaced as well.

Location differences due to parallax displacement are easier to correct than those caused by pixel shape. Parallax displacement can be corrected if the height of the feature can be estimated, however there is no sub-pixel information to locate a point somewhere other than the pixel’s center point.

By Carl Dierking & Grace Veenstra

2025 Fire Points Video

Abigail Haas — Tue, 05 May 2026 22:45:38 +0000

The 2026 wildfire season is underway in Alaska, with an official start date of April 1, 2026. As we look ahead to this summer, let’s take a look back at last fire season. This animation shows a time lapse of fire heat points detected by GINA over the course if the 2025 Alaska fire season, from May 17 to August 17, 2025.

Recent fire points are displayed as bright red and fade to brown after a few days. Fire points frequently appearing in the same location indicate an actively burning wildfire. This product doesn’t detect points that are under cloud cover or thick smoke. This product is used by the Alaska Fire Service for detecting active fires, which can be viewed on the Alaska Interagency Coordination Center Dashboard.

Check in on GINA’s homepage for updated fire points videos, which will be posted weekly during the 2026 season or visit GINA’s wildfire resources page to delve deeper into satellite remote sensing of wildfire and stay up to date as the 2026 season progresses.

Reviewing the Satellite Parallax Problem

Grace Veenstra — Sun, 19 Apr 2026 22:51:48 +0000

If you look at something close to your face, you might notice its location seems to change depending on whether you have one or both eyes open. Perhaps the object seems to move left or right relative to the background, without even moving your head. This is what is known as parallax, the apparent difference in the location of an object based on point of view.

With satellite data, parallax is always something important to consider, because the features you see may not actually be where they appear to be. Parallax can cause meteorologists to misidentify the location of a storm cloud top or wildfire plume. In order to account for parallax, it’s necessary to understand the orbital characteristics of the two basic types of satellites for meteorological data — geostationary and polar — since that is what determines their viewing angle over Alaska. Once aware of how parallax affects satellite data, we can get to work correcting the displacement.

The following is a three-part series from 2022 by GINA’s satellite liaison Carl Dierking discussing parallax and its affect on satellite data in Alaska.

An overview of parallax in the context of satellite imagery. It reviews the differences in orbital characteristics of geostationary and polar satellites.

The section reviews the parallax issues associated with geostationary satellites, and the relevance to polar latitudes.

This section reviews the parallax issues and considerations that need to be taken into account when working with polar satellites and their data.

By Grace Veenstra

Identifying Low Clouds & Fog in Satellite Imagery

Abigail Haas — Thu, 16 Apr 2026 00:07:06 +0000

In Alaska conditions can change quickly. Fog, snow, ice and clouds all impact transportation, on the ground and in the sky. Pilots rely on weather forecasts and reports to determine if it’s safe to even take off and to stay safe once in the air. Ground-based weather observations show conditions at a specific point and time, but satellite observations can show the extent and evolution of low clouds and fog over a large area. These two types of observations are then used to make short term predictions about how these conditions will change at the surface.

An example from April 3, 2026 shows how satellite imagery can be used in tandem with ground based observations any time of day. A series of images, shown as an animation below, captured by NOAA satellites pictures low clouds and fog moving across the North Slope of Alaska and the Arctic Ocean, first in the darkness of the early morning and then in the light of day. Two satellite products are used to accomplish this around-the-clock coverage. The animation begins at 11:30 UTC, 2:30 am Alaska time, first using the VIIRS Nighttime Microphysics RGB to see cloud progression in the dark, then switching to the VIIRS Day Snow/Fog RGB at 16:30 UTC as the sun comes up.

The VIIRS Nighttime Microphysics RGB is a product designed specifically for night observations. Low clouds and fog take on a dirty yellow to light grey color, while high clouds appear magenta to red depending on thickness, while the snowy ground and sea ice take on a pink to orange appearance. Low clouds and fog are generally lumped together when describing satellite imagery as it is very difficult to distinguish them without the help of ground observations. In this RGB from the polar orbiting satellite the green component is assigned to the common fog channel difference of 10.8μm – 3.7μm, and the red component is assigned to the channel difference of 12.0μm – 10.8μm to assess cloud thickness. While the blue component is assigned to the 10.8μm to enhance areas of warm (i.e. low) clouds where fog is more likely.

One of the challenges faced in winter daytime satellite observations is to distinguish white “reflective” snow from white “reflective” clouds on visible imagery, this is when the VIIRS Day Snow/Fog RGB works its magic. Fog and low clouds appear in a bright lavender or white color, while snow covered ground and sea ice appear in a contrasting bright red. The reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared spectrums. The channels which bring out the distinguishing differences are combined in the Day Snow/Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

Together these satellite products provide an excellent overall picture of what low clouds and fog look like and how they are moving. Ground based observations can then confirm what is occurring at several locations along the north gulf coast. These tandem observations can help assist forecasters particularly when it comes to writing Terminal Aviation Forecasts (TAFs).

Table 1 shows ceiling and visibility observations from different ground stations at corresponding times to the animation. Red highlighted cells indicate IFR, Instrument Flight Rules, conditions and blue cells indicate MVFR, Marginal Visual Flight Rules, conditions. In aviation the IFR category is used in conditions with a visibility of 3 miles or less and ceiling of 1000 ft or less. MVFR is for a visibility of 3-5 miles and/or a ceiling of 1000 to 3000 ft. When weather conditions improve beyond MVFR, pilots can operate under VFR, Visual Flight Rules.

Under IFR pilots rely on the navigation instruments in their airplanes to safely fly and land. Not all aircrafts are equipped to fly in those conditions, making accurate forecasting even more important. Monitoring the evolution of stratus and fog in satellite imagery combined with observations made on the ground help forecasters make short term predictions about when IFR conditions might improve, keeping pilots informed and safe.

Written by Abigail Haas and Carl Dierking

You can Help GINA and Collaborators Better Identify Blowing Snow Hazards!

Abigail Haas — Fri, 20 Mar 2026 00:23:32 +0000

Alaskan’s are used to winter weather impacting their daily commutes, from cold weather and snow storms, to icy roads and ice fog. However, one hazard that is often overlooked in blowing snow. Blowing snow is snow that has already fallen and is lofted into the air by strong winds. Severe blowing snow events can reduce visibility to near-zero conditions, posing a significant danger to both ground and air transportation.

Forecasting, detecting and monitoring blowing snow can prove challenging with the addition of variables like wind speed and current snow pack. That’s why satellite remote sensing is key to understanding and forecasting blowing snow conditions.

Over the past several months GINA has been collaborating with NOAA NESDIS’ Center for Satellite Applications and Research (STAR) and the citizen scientists from Global Learning and Observations to Benefit the Environment (GLOBE) Program to test run a new satellite product designed to detect blowing snow. This project combines satellite products for detecting blowing snow with ground images provided by citizen scientists to validate the satellite imagery.

Blowing snow can be detected by satellites using the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Joint Polar Satellite System (JPSS) satellites and the Advanced Baseline Imager (ABI) instrument aboard the Geostationary Operational Environmental Satellites (GOES-R series). Both the GOES ABI and JPSS VIIRS use a combination of near-infrared and infrared imagery to highlight blowing snow lifted and transported by strong winds and to distinguish it from surrounding snow and cloud cover.

Even with this technology there are a few challenges that make detecting blowing snow with satellites difficult. For example, blowing snow satellite products rely on reflected sunlight, meaning that blowing snow can only be detected during the daytime and depends greatly on solar illumination, sun angle, snow plume thickness and clear skies. Another challenge is distinguishing blowing snow from clouds. At times blowing snow can appear cloud-like in satellite imagery making it hard for forecasters to use common satellite imagery techniques. This project aims to use citizen science data to confirm the presence of blowing snow in satellite imagery, thus improving and refining blowing snow satellite products to support NOAA’s National Weather Service forecasts and warnings.

Learn more about this project and its collaborators and how to report your own blowing snow observations here!

This project was developed in collaboration with the National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS), the Geographic Information Network of Alaska (GINA) at the University of Alaska Fairbanks, and the GLOBE Implementation Office Science, Training, Education, and Public Engagement team (STEPE) at City University of New York.

Visualizing the southerly extent of Bering Sea Ice with VIIRS

Abigail Haas — Thu, 22 Jan 2026 01:15:53 +0000

This VIIRS DayLandCloud RGB captures the southerly extent of Bering Sea ice of the south western coast of Alaska, as of January 14, 2026. The DayLandCloud RGB is used to distinguish snow and ice from clouds. Sea ice is pictured in darker blue while snow on land is captured in a brighter cyan.

Like the VIIRS DayLandCloud RGB this SnowCloud Discriminator is used to distinguish ice and snow from clouds. However, unlike an RGB, the SnowCloud Discriminator uses the Day/Night band (DNB), meaning it can make observations in the dark!

GINA Science Education Director becomes GLOBE Education Deputy Director

Grace Veenstra — Thu, 19 Jun 2025 00:37:39 +0000

Christi Buffington, who joined GINA last year as our science education director, was recently promoted to deputy director for Training, Education, and Public Engagement at the GLOBE Implementation Office! Amazing Christi, we’re so proud!

In the time she’s been with GINA, Christi has quickly become a valuable member of the team. She’s brought GINA a new component of outreach and education and geospatial research using participatory science, giving us a richer connection to Alaskan communities and ground-based observations.

GLOBE (Global Learning and Observations to Benefit the Environment) is a science and education program that has been implemented in 127 countries, and includes students, researchers, educators, and many many more. The GLOBE database has gathered over 270 million measurements made by over 1.2 million participants. GLOBE is led by NASA and supported by NOAA, NSF and the U.S. Department of State.

Some GLOBE data are ingested into MADIS (Meteorological Assimilation Data Ingest System) which can be used by meteorologists and researchers to aid in their forecasting.

To learn more about Christi’s work and GLOBE, check out this article from the University of Alaska Fairbanks, the GLOBE website, and the website of the GLOBE Implementation Office.

GINA’s New Antenna

Grace Veenstra — Fri, 11 Oct 2024 19:54:24 +0000

On April 6, 2024, GINA’s newest antenna – dubbed “UAF-5” – was lifted into place atop the University of Alaska Fairbanks’ Usibelli Engineering Building. On September 6, 2024, the installation of UAF-5 was completed with the addition of it’s protective radome. This 3-meter antenna will replace the “Big Dog” antenna atop UAF’s Akasofu Building, which will be repurposed for educational activities, but for the moment, both “Big Dog” and “UAF-5” capture dozens of satellite passes per day.

Aerosol Optical Depth Product in Use for Monitoring Smoke

Michelle Morris — Tue, 25 Jun 2024 23:05:29 +0000

Fire season is in full swing here in Alaska! The TrueColor RGB shows a scene similar to what the eye would see. Smoke is a wispy light grey color while clouds are brighter white. The Aerosol Optical Depth (AOD) product is designed to provide a more quantitative view of smoke, ranging from thin in green to thick in red. Blues and purples are not significant. The AOD cannot identify smoke in the clouds.