A Planet Four Update
Today we have a post by Candy Hansen, principal investigator (PI) of Planet Four and Planet Four: Terrains. Candy also serves as the Deputy Principal Investigator for HiRISE (the camera providing the images of spiders, fans, and blotches seen on the original Planet Four project). Additionally she is a member of the science team for the Juno mission to Jupiter. She is responsible for the development and operation of JunoCam, an outreach camera that involves the public in planning images of Jupiter.
Dear Planet Four Volunteers,
Years ago we enlisted your help to measure spring fans on the surface of Mars’ seasonal CO2 polar cap. Our small science team had a vision of what we could learn from those fans about the weather on Mars, but we did not have the resources to make the needed measurements.
With your investment of your time we now have an extensive catalog of fan measurements. The catalog crosses space (the Mars south polar region) and time (8 years of Mars southern spring images). The contents of the catalog and potential science use was documented in a paper by Michael Aye, published in Icarus in 2019. We have a new paper by Anya Portyankina, in press at the Planetary Science Journal, that compares wind direction and speed (from your measurements of fans’ orientation and length) to one of the standard Mars weather models. From this we can explore where the models do and do not do well to predict the weather. This very important goal that we envisioned from the beginning has now been achieved thanks to your generous donation of your time to make these measurements.
Over the years we have realized that seasonal activity is affected by Mars’ dust storms. Using the catalog we can quantify the differences in the number and size of fans after a dust storm. That work is now underway so we have posted more images and we hope you will help us again by making more measurements. We know your time is valuable and we sincerely appreciate your willingness to help with this analysis of the weather on Mars.
One final note – we have explored doing this task via machine learning. People still win!