Hi! My name is Ianjon Brower and I’m working for ASRC-Federal and GINA this summer. My project for this summer is going to be prototyping a Raspberry Pi with specific sensors that monitor the temperature (and other things) of ice cellars that is easy for anyone to use! These ice cellars are in native villages such as Utqiagvik, Nuiqsut, and many more. The reason we need to do this work is that some of the ice cellars have been filling up with water by melting and this is ruining their storage for all year around.
What’s in an ice cellar you ask? It’s use to store all the hunted game that’s found locally to preserve. It’s a natural big freezer that’s supposed to stay ice cold all year around. We are going to get some data to figure out why this is happening! I have a lot of learning to do for this project and the people here are a great help.
UAF GINA recently orthorectified historical aerial photography from 1950 and 1984 covering Anchorage for the Alaska EPSCoR program. These images are useful for conducting a variety of change detection analyses on the landscape. The image data is freely available by contacting UAF GINA at: email@example.com.
We recently made a number of improvements to the MODIS and VIIRS images we distribute via GINA Puffin Feeder.
For VIIRS on SNPP, we made a number of improvements.
First, the day night band (DNB) imagery has switched to Curtis Seaman’s ERF scaling, which should provide a much more useful product.
These images look a bit dark, but keep in mind the lower left half of the image is in complete darkness.
A floating point version of this product with no scaling applied is also available, which is useful to retrieve details lost in the scaling process. Please contact us this product might be useful to you.
Second, we changed to a different algorithm for generating the natural color and other RGB composites - they should look similar, but not exactly the same. Please let us know if you notice any differences that affect the product’s usefulness.
Finally, we changed how the thermal product is scaled. This should allow for a more useable product, and allow conversions from the data from Feeder to brightness temperatures. The major thing to note is “cold” values are now white, and “hot” values are now black. Please see the note below for more details.
For MODIS, we have changed how the natural color and other rgb products are generated, and while they might look different, they should look similar. Please let us know if the differences affect the usability of the product.
One major change is switching to providing band 31 for the thermal images. Band 31 contains the 10.780µm - 11.280µm region of the spectrum, and should provide a better long wave IR image.
We have switched to scaling the images the same way we do for AWIPS, where cold is white, and hot is black. To convert the pixel values back to brightness temperatures, you can do something like this:
If the value is greater than or equal to 180:
Temperature in Kelvin = 418.15 - pixel_value
Temperature in Kelvin = 328.15 - (pixel_value/ 2.0)
Black (1) is 327.65 Kelvin, or 54.5 Celsius, white (255) is 163.15 Kelvin, or -110 Celsius.
Finally, these products are now produced via our NRT Dashboard . We have a API that allows programmatic retrieval of these products - please drop us a line at firstname.lastname@example.org if you have a need for it.
Questions or comments? We can be reached via email at email@example.com .
Volcanic eruption monitoring using both geostationary (Himawari) and polar orbiting (SNPP) satellite imagery, RGBs, and derived volcanic ash products.
With days getting longer and daily high temperatures tending to occur in the afternoons as opposed to at random hours day or night, spring breakup in Alaska cannot be far away. GINA receives satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS), and this data is then turned into several kinds of satellite imagery useful for the National Weather Service (NWS).
One such product, shown below, is designed to highlight floods associated with spring breakup. This is a screen capture from the NWS’ operational workstation at the River Forecast Center in Anchorage. The big image is derived from GINA’s VIIRS data. Since Alaska is still mostly covered with snow, this product is still mostly white. But as the snow melts, the white will recede, and any overland flooding will be shown in hot colors ranging from yellow to fire-engine red.
Get your breakup boots out of the closet and stand by for further updates as Spring Breakup 2017 gets underway.
The Suomi National Partnership Program (S-NPP) weather satellite flies over Alaska several times a day, and shortly before noon on Saturday, February 25 caught the above image highlighting the classic “mackerel sky” over Alaska’s Interior composed of parallel rows of altocumulus clouds.
While snow, clouds, and ice all appear white to the human eye, the wavelengths of light and energy detected by the S-NPP weather satellite can be combined to depict clouds as pink, and snow and ice on the ground appear as cyan. In scenarios like this, the lower the clouds are, the more pink they look. Clouds at higher elevations appear wispier and include overtones of white or even blue tints. A zoomed version of the image is included below and better shows these amazing clouds.
This type of satellite imagery requires that sunshine bounce off the clouds and the landscape, so as the sun returns to Alaska with the transition from winter to spring, this kind of satellite imagery will be available more and more often.
For additional examples of this imagery, and other kinds of imagery as well, check out GINA’s “Puffin Feeder” website at http://feeder.gina.alaska.edu/ More information about the S-NPP satellite and the new series of Joint Polar Satellite System (JPSS) satellites to be launched in the future can be found at the website http://www.jpss.noaa.gov/