LoneSky

Broken links are annoying. In order to better keep track of the constantly changing download URLs of the various satellite data referenced in this blog, this page has been created to host the most recent known working server locations. [UPDATE: May 29, 2026] Added DSCOVR EPIC data download information. [UPDATE: June 11, 2026] Added Chollian-2A data download information.   NOAA GOES-R SERIES For full-disk images in pseudo-true color, these are the two most useful products: Product: ABI-L1b-RadF Format: Individual bands; 10848 x 10848 resolution or higher. Product: ABI-L2-MCMIPF Format: Combined bands; 5424 x 5424 resolution. GOES-16 (EAST) ABI Download — 2017-2025: https://noaa-goes16.s3.amazonaws.com/index.html#ABI-L1b-RadF/ https://noaa-goes16.s3.amazonaws.com/index.html#ABI-L2-MCMIPF/ GOES-17 (WEST) ABI Download — 2018-2023: https://noaa-goes17.s3.amazonaws.com/index.html#ABI-L1b-RadF/ https://noaa-goes17.s3.amazonaws.com/index.html#ABI-L2-MCMIPF/ GO...

Here is my current Python script for GOES-16 and GOES-17 satellites. If you followed the GOES Satellite Data Download tutorial and saved the file as suggested you need only change the path variable in the script. For example, if your path looks like this: E:/goes-16-data/full-disk/multi-band/2020/0415/1430/ The path variable will be this: path = 'E:/goes-16-data/full-disk/multi-band/' Do not include the YYYY/MMDD/hhmm/ part of your path; the script assumes you used this directory structure and will handle it automatically. Also don't forget the quotes and forward slash at the end!

GOES-16 and GOES-17 have no green band so one has to be created from the blue, red, and near-infrared channels. But no amount of simple blending of those channels will produce a truly accurate synthetic green band so additional processing—e.g. a look up table (LUT)—is usually necessary to further compensate. For demonstration purposes I'll be using mostly Himawari-8 imagery in this post. Himawari and GOES share the same imager with reasonably similar blue, red, and near-infrared bands. The big difference, of course, is Himawari includes a visible wavelength green filter (though at a wavelength that is not ideal for true color). GOES prioritizes a unique near-infrared filter in place of a green one. By combining blue, red, and near-infrared channels Himawari will produce approximately what GOES is capable of. This can then be compared to Himawari imagery using its RGB filters (with a little near-infrared added to boost the vegetation signal). Here is a commonly cited GOES synt...

Data from GOES-16 and GOES-17 is free and easy to obtain, especially since Brian Blaylock created an excellent interface to download the multitude of available products from these satellites. [Update: May 28, 2026] Use the satellite data download page for working links. DOWNLOAD GOES DATA Start by going to the link here: http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/cgi-bin/goes16_download.cgi For this tutorial we are primarily interested in full disk GOES-16 data. We will be using imagery from the same day as in the Himawari 8-tutorial but this time at 14:30 UTC. As with Himawari-8, we will download 2000-meter resolution data. The difference is all 16 available bands are contained in a single file rather than four individual files. From the Domain drop-down menu select Full Disk . From the Product menu select ABI L2 Cloud and Moisture Imagery (Multi-Band Format) . From the Date menu select 04/15/2020 . From the Hour (UTC) menu select 14 and click the Submi...

In preparation for working with free, publicly available Himawari and GOES satellite data first you will need to install Python on your system. Python is a high-level programming language, but let me stress that you will not need to do any coding yourself; the necessary scripts will be provided in subsequent blog posts. This tutorial is for Windows 10 but Python is available for other operating systems. The installation procedure is relatively straightforward even when installing in a sandbox environment. We will be using a free minimal version of Anaconda—a Python data science platform—called Miniconda, along with a few necessary packages needed to read and work with the satellite data. DOWNLOAD MINICONDA Download Miniconda3 Windows 64-bit (Python 3.7 version) here: https://docs.conda.io/en/latest/miniconda.html INSTALL MINICONDA Run the installer you just downloaded. You can choose to install Miniconda for all users or just the current user. I chose Just Me from the menu...

This is an approximately true-color image of Earth as captured from the GOES-16 (now GOES-East) geosynchronous satellite. The image was acquired on August 8, 2018: Earth as imaged by the GOES-16 satellite. The image was first processed using a Python script I wrote then imported into a paint program to make the final color adjustments. I hope to detail the procedure in a future blog update so others can experiment with it. Unlike GeoColor imagery, the intent here was to present a view closer to what a human observer would see. Unfortunately because no green filter was included on GOES-16 it is not possible to make a true-color image. The precursors to GOES-16—Himawari 8 and 9—do have a green filter (though not at the ideal wavelength) and are capable of more true to life views. Despite optimism that a work-around method would be utilized to generate a more accurate pseudo green channel and thus produce images closer to true color, so far I have seen no indication that it has...

This is a two-day color-enhanced composite from GOES-16 taken June 2-3, 2017 (17:45:00 UTC) with simple cloud reduction processing. The individual image bands were provided by SSEC RealEarth . Credit: NASA / NOAA / SSEC - Modified by LoneSky.

This is an approximately true color composite image of the contiguous United States as captured by GOES-16 on May 17, 2017 (17:15:00 UTC) and remapped to Mercator projection for SSEC RealEarth . Credit: NASA / NOAA / SSEC - Modified by LoneSky.