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 Himawari-8. If you followed the Himawari-8 Data Download tutorial and saved the files as suggested you need only change the path variable in the script. For example, if your path looks like this: D:/himawari-8-data/2020/0415/0500/ The path variable will be this: path = 'D:/himawari-8-data/' 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...

The Australian Bureau of Meteorology (ABOM) has made Himawari-8 data freely available to the public as far as I can tell. The agency has posted fully accessible links to the files at NCI via THREDDS . Although there is some contradictory wording about whether or not you need to register, you will not be prompted to do so. ABOM does require citation but this is a quite reasonable request. DOWNLOAD HIMAWARI-8 DATA Start by going to the link here: https://thredds.nci.org.au/thredds/catalog/catalogs/ra22/satellite-products/arc/obs/himawari-ahi/fldk/fldk.html [Update: May 27, 2026] All THREDDS server links have been updated. Data is available from as far back as 2015. For this tutorial we will be downloading full-resolution files from 05:00 UTC on April 15, 2020, a particularly clear day over much of China. This will make a 11000 x 11000 image. [Update: March 22, 2024] It appears that only full-resolution files are available now. That means blue, green, and infrared bands a...

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 cool: Chaidhat Chaimongkol has made a Java applet that downloads the latest Himawari-8 imagery from the Japan Meterological Agency and applies color correction based on or inspired by my post on this very subject. It then saves it as a desktop wallpaper, continuously updating as new imagery is released. Himawari8SatDesktop Java applet by Chaidhat Chaimongkol. I have not tried it yet as it will require some version of Java runtime environment. I'm looking for a portable implementation that will not need to be installed in Windows 10, possibly jPortable64 or OpenJDK JRE Portable 64-bit . Here is an animation, presumably made from individual frames: Download the Himawari8SatDesktop applet here: https://gist.github.com/Chai112/5006fc94c98d22cae598749c7de86717

Earth as imaged by Himawari-8 at nearly solar noon over Japan (11:50:00 JST / 02:50:00 UTC) on April 12, 2017. An experimental yet relatively simple method for intuitive color correcting the full resolution imagery presented by NICT ScienceCloud has been applied to approximate how the planet would appear to our eyes. SOUTH ASIA Southeast Asia and the Tibetan Plateau are seen in this rotated view. Note the persistent haze at the foot of the Himalayan Range. The mountains and plateau restrict the movement of precipitation (as well as aerosols) from the south resulting in the rain shadow deserts of central Asia. Credit: MSC of JMA / NICT - Modified by LoneSky.

This post will detail an intuitive (i.e. non-scientific) color correction method for Himawari-8 web imagery. It was updated on May 12, 2017 with additional information on the revised steps used to modify full resolution data downloaded from NICT ScienceCloud (accessed from this archive page ). [Update: January 18, 2022] The JMA link below now leads to JAXA Himawari Monitor and the product in no way resembles the original example. Credit: MSC of JMA / NICT - Color correction by LoneSky.