LoneSky

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!

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...

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. 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 Submit button. You will next be presented with option to choose the starting minute ...

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://dapds00.nci.org.au/thredds/catalogs/ra22/satellite-products/arc/obs/himawari-ahi/fldk/fldk.html 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 are at 1000-meter resolution, while the red band is at 500-meter resolution. T...

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 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

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.

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.

Earth from approximately 1 million miles taken by the DSCOVR spacecraft orbiting around Lagrange point L1. The original composite photo (see sample below) has had curves applied in post processing. Sample: https://epic.gsfc.nasa.gov/archive/natural/2015/10/12/png/epic_1b_20151012074645.png Mission information: https://www.nesdis.noaa.gov/DSCOVR/ Daily product: https://epic.gsfc.nasa.gov/ [Update: May 11, 2020] Sadly, it appears all the natural color images have been reprocessed using poorly implemented atmospheric correction. There is now a nasty fringe artifact around the shadow side limb and the entire image has an unnatural yellowish/grayish cast. I really don't understand this need to overprocess Earth imagery. What is wrong with seeing the atmosphere as we would see it? There is already an enhanced color version with more aggressive correction applied. This feeble reprocessing of the natural color images does nothing to improve the appearance or add to our und...