I thought it was time to add a little intrigue. Below is my favorite picture from the the ever-growing stack of cloud photos (see right…). We’ll call it the unofficial 2019 photo contest winner. This one is of a rain shaft falling from a thunderstorm over New Jersey taken by Lea Tong. What I think is especially interesting is all the subtle structure in the rain shaft. What? Don’t see much structure that you think warrants any explanation? Ah, just wait for my next “forgotten cloud physics” paper to come out some time in 2020! It’ll “blow” your mind.
NJ Downpour © Lea Tong
Happy Thanksgiving, everyone.
A few days ago, a colleague from UCD Mathematics, Joseph Biello (of MJO fame), and I published a paper in npj Climate and Atmospheric Science. It’s available here. We attempted to use well understood aspects of the statistical properties of precipitation to derive a simple model for global precipitation. Then we populated model parameters with data from satellites and from cloud simulations. The nice thing is that our model is analytic, and because its constructed from physically understood pieces, it’s very easy to use as a sandbox. In the paper, we ask what possible future states of precipitation may look like given some arbitrary surface warming. The cool (or not so cool depending on your perspective) result is that many possible future hydrologies are possible. Yet models seems to predict a limited range of possible responses in the properties of precipitation. This could mean two things. 1) There are some consistencies among models that are physically based that we have not identified or 2) the suite of models is not large enough to span the possible responses. My gut feeling is that its probably a little of both. What I think is neat about this paper is that we have created an analytic, data-driven model of the climatology of precipitation properties. We’re hardly the first to do such a thing, but this one is mine.
I just wanted to point out all the new cloud photos on this website (look right). Special thanks to ASGG students Katherine Chin, Jenae Clay, and Megan Schmiedeler for contributing some excellent photos from all over the world. And more thanks to John (dad) and Jen (sister) for their contributions. If you’d like to contribute your own photos to the gallery, send me an email. My plan is to continue to grow this gallery with photos of interesting and unique cloud formations. My next mission is to reach 100 photos. Please use these photos with proper acknowledgement to the copyright owner if one is listed.
This is more than just a pet peeve. In many papers I review (I won’t attempt to blindly guess what fraction, but it’s high!), authors plot ratios of terms poorly. Let’s say you want to know the relative importance of the magnitude of longwave radiative cooling of a cloud top to the shortwave radiative warming. A reasonable way to assess such a thing would be to take their ratio and plot it up (over space or time or whatever). The common way (and I will argue the wrong way) one might visualize this is to plot up the ratio on a linear scale.
I’ve plotted up some synthetic data below. A and B are MATLAB vectors initialized with 100 random numbers between 0 and 1. The histograms of A and B are shown in the top row below. They’re nearly uniformly distributed. Now if I want to know something about the ratio of A to B, I might plot it up like I did on the bottom left. And, I might ask what the mean ratio is. It’s 4.3. This combination of information might lead me to conclude that the ratio is characterized by something greater than 1. Seems reasonable. But remember, I have two randomly initialized vectors; I would expect their properties to be about the same. I’m more likely to appreciate that with the figure on the bottom right. By taking the log, I give equal voice to things less than 1 and greater than 1. Excursions above the dashed line (at 1) and below occur with about the same frequency and magnitude. The mean of log10(A./B) is 0.06; When I raise 10 to that power (i.e. 10^0.06), I realize that my average ratio is better characterized by 1.1 (Or, about even odds of my random variables being greater or less than one another).
I’m getting tired of harping on this in reviews.
Yesterday’s rain was pretty noteworthy. I got 0.93″ at my CoCoRaHS gauge. That would make yesterday the 3rd rainiest May day of the last 50 years here in Davis. It also instantly jumps us to being the 10th rainiest month of May over the last 50 years. With more rain predicted today and next week, this could easily end up being one of the top 5 rainiest Mays ever.
Below is a bar graph showing the sorted daily rainfall totals for May days over the last 50 years. Notice a few things: 1) the logarithmic axes, 2) X-axis values start at day 1412 because 1411 of May days over the last 50 years have seen 0″, 3) the values nicely fall along a line in the log-log plot (except for the very heaviest days). This last point is an example of ubiquitous “power-law scaling” in the Earth system in which intense occurrences are very, very rare but also very much more intense than common events (think something like the great San Francisco Earthquake compared to the weak shaking SF feels commonly). Yesterday’s rainfall didn’t amount to the quake of 1906…more like the Earthquake of 1989.
I just had a conversation with Phil Klotzbach (who by no means endorses this post) about, among other things, the life and legacy of Bill Gray who died three years ago yesterday (https://en.wikipedia.org/wiki/William_M._Gray) . I didn’t know Bill Gray except as the emeritus professor who showed up to seminars at Colorado State with a back pillow and a lot of questions. That is to say, I didn’t know him personally. But I did try to appreciate his scientific perspective as a student, and I still try to today. Bill Gray had an unbelievable intuition based on decades of experience for how the tropical atmosphere works. Bill Gray was also a noted climate change skeptic which is a considerable shame. He will clearly go down on the wrong end of that. Where I think Bill probably deserves to be heard is on his criticism that many of us today who study the atmosphere have a profound lack of appreciation for the messiness of real world meteorology. Just because a one-month mean map implies that anomalous high pressure existed somewhere doesn’t mean that high pressure existed in that place every day that month. I often hear Bill yelling at me in the back of my mind when I use a mean sounding to initialize a convective simulation. Did the conditions implied by the mean DYNAMO sounding every actually occur during DYNAMO? I don’t know. And, I’ve never looked. I’ll go down on the wrong end of that one.
We had some nice rain here in the Central Valley over the weekend. The mountains got some serious snow. This is the CoCoRaHS map for Yolo County for this morning (7am 2/3 to 7am 2/4). I wanted to point out the awesome precipitation gradient here in Davis. The west side of town got >1″ of rain while those of us on the east side got <0.4″. That’s a distance of just a few miles of flat terrain. This is why mesoscale networks (like CoCoRaHS) are so useful and critical to our understanding of clouds and precipitation.
Really quickly, I wanted to start off 2019 by announcing the success of some students. Hrag Najarian was selected to give one of only four special undergraduate talks during next week’s 99th Annual AMS Meeting in Phoenix. See his talk on moisture and precipitation on Tuesday at noon: https://annual.ametsoc.org/index.cfm/2019/programs/events/undergraduate-presentations/ . Nick Falk submitted a paper on the work he, Adele, and I did over the summer to MWR. And finally, high school student Ameya Naik of Mira Loma High School who came to chat with me about his really interesting work on tornadoes in the Central Valley recently won an honorable mention for his AGU Virtual Poster: https://education.agu.org/virtual-poster-showcase/recognition/2018-virtual-poster-showcase-winners/ . Great work all around.