Take a look at the radar image taken from a few minute ago. See that blob of heavy rain south of Fairfield? Look at any Sacramento radar image when it’s raining somewhere in the Central Valley and this spot will always be raining heavily.
What’s up with that? Is it local rainfall enhancement from Grizzly Bay plus some orographic enhancement? Luck? Government conspiracy? Alien activity? Let’s take a look at the location from a satellite.
OK, looks like maybe we have a transition from nice, green state park land to browner farm land near the location of the “rain blob”. So maybe there is some kind of surface-type-transition thing going on. Let’s zoom in.
The region has some brown fields and some green fields (some “fair fields” one might say?) and some odd veining. What’s causing that? Let’s zoom in again.
Oh, they’re wind mills. Are windmills secretly making it rain? Well, maybe, but probably not. What happening here is that the radars are seeing the windmills. Normally when a weather radar sees a very tall solid object, like a mountain, sticking up into the air it can filter out that reflection 1) because the mountain has zero velocity and 2) because a mountain has always been there. But radars can get tricked by windmills. They look like really big raindrops!
Another AGU Fall Meeting is in the books (for me at least…it doesn’t actually officially end for another few hours). Thanks to everyone who came to my talk yesterday and for the useful feedback on potential uses of the new method I introduced. I’m looking forward to seeing everyone again next year in DC.
One of the great things about the AGU meeting is the diversity of science presented. I’ve really become a fan over the past few years of the Nonlinear Geophysics section. The award for the presentation that taught me the most this year goes to NG34A-07 (for those of you who don’t speak AGU: Nonlinear Geophysics’ Wednesday evening’s 7th presentation) on “Efficient simulation of tropical cyclone pathways with stochastic perturbations”. How does one compute something like the 99th percentile of a distribution of simulations without conducting at least 100 simulations? The easy answer is that you can’t. But the hard answer is that you can do some clever rescaling. If we’re interested in the most intense storms we can simulate given some set of conditions, run 8 (or whatever) simulations 25% of the way. Select the most intense of those and discard the rest. Give those simulations some stochastic noise and continue. Then repeat. In the end, you’re left with the most intense storms only. That would only be mildly informative expect that the presentation also included a way in which to determine what the actual likelihood of those simulated storms is given the initial weak storms. Abracadabra, you can find the 99th percentile (in a way affected by stochastics). Very cool!
Fair warning: this post doesn’t really have a point, is incomplete, and is the result of my staring too long at data without taking a reasonable break.
As a cloud person, I typically divide the atmosphere up into small volumes of “clouds” and small volumes of “clear air”. 1 or 0. Cloudy or not cloudy. Yes or no. But I’m slowly changing my thinking. Let’s consider a very silly and wrong thought experiment. If I have two “clouds” very far apart from one another, it is very easy to have a region in between these clouds with low humidity. Cloudy and clear air. But what if these clouds are infinitesimally far apart? Is the interstitial air cloudy or clear? It’s probably neither..at least not in the way that the clouds are “cloudy” and the air far from a cloud is “clear”. So, let’s call it cloud-like. Well, now we have a new problem, when is air no longer cloud-like but clear? The common phrase, echoing a famous Supreme Court ruling, is that “I know a cloud when I see it”. Now, that’s clearly a quip and not literal, but I’ve always thought that it introduces an issue implicitly — that a cloud is something radiative (such that we can “see”) it. But, in principle, a cloud could exist in a world without gradients in radiative fluxes. It would be invisible, but would still be a cloud. Does a cloud need upward velocity? No. Does a cloud need liquid water? Probably, but is it one isolated drop a cloud? Are 50, or 5 million? Ultimately, we all just set some threshold on some parameter we can measure to define what cloud is. But the non-cloud is likely to be cloudy by a less strict definition. So what is the space between clouds?
From hurricanes to fire weather, it’s been a busy couple of weather weeks. So far, Davis has been spared any real impact from the fires here in CA. It’s been a little smoky, but that’s nothing. There has been a lot of discussion recently about the weather situation here in CA. I just wanted to talk a little bit about rainfall. It’s dry here in the Central Valley in the summer. This year has been no exception to that rule, but I have seen several media outlets implying that the current fire weather situation has something to do with unusual dryness this summer. So, has it been unusually dry this summer?
I pulled 50 years of precipitation data from the weather station at UC Davis’ Campbell Tract. Panel a) in the figure below shows the mean and median rainfall from the Campbell Tract and our rainfall from 2017. May and June 2017 saw above-median rainfall, but we’ve had no measurable rain since June which is in keeping with the median rainfall. Panel b) shows the fraction of years with *any* measurable rainfall. Over 90% of all Julys over the past 50 years have seen zero days with rainfall, and only about half of all Junes and Septembers see even one day with rainfall. So our not having seen any rain since May is far from unusual even if the headline that we are tied for the driest June-September ever is technically true. I added panels c) and d) just for fun. Panel c) tries to show the impact of climate change (in an extremely unscientific way) by comparing median rainfall between 1968-1993 with median rainfall from 1994-2017. One could conceivably infer that climate change has resulted in wetter springs and longer rainless summers. Panel d) gets into some daily data. It shows the percentage contribution to the 50-year total monthly rainfall by individual days. The most extreme story comes from July where rain has fallen on just 11 July days over the past 50 years! One day in July 1985 contributed 40% of all July rainfall over 50 years!
So long story short, rainfall here in the Central Valley (in Davis which has admittedly seen no fires) has not been unusual.
There is nothing I can say scientifically or socially about hurricanes that hasn’t already been said in the past two weeks. So, I won’t try. Many colleagues are much better able to discuss individual hurricanes than I am. While their destructive force can bring and, recently, has brought incomprehensible misery to those living in their path, hurricanes stands out starkly as almost unbelievable. They are fundamentally so different than anything else in meteorology. So much has to go just right in order for a hurricane to form that I think it’s easy to take their existence for granted. Hurricanes need the perfect combination of moisture (which the tropics have plenty of), initial convergence (which, again, the tropics has plenty of), and spin from Earth (yeah, that doesn’t ever change but only occurs in a narrow band near the equator). Yet, hurricanes are remarkably infrequent, even if at the present, they don’t feel that way. Hurricanes are simply a marvel of nature. The strongest often form nearly perfectly circular clouds that stretch for more than 500 miles and winds that howl at over 150mph interrupted only at their center by cloud-free quiescent air in their stadium-like eye. Hurricanes are amazingly simple: they can be described well with just a few equations derived from simple mechanical physics. Yet, hurricanes are amazingly complex: the world’s best computer models still fail to simulate their growth and decay with the desired fidelity. Hurricanes are often symmetric yet made up of turbulent, chaotic, transient flows. They’re almost unbelievable.
I wish all my friends and colleagues back in Miami the best of luck this weekend.
Meteorology, atmospheric science, and climate system science is each classified as an applied science. That means that these fields are concerned, as much as anything, with practical, real world problems –making better weather forecasts of hurricane Gert or of cloudiness during the next solar eclipse, or making more accurate predictions of future climate states. These are important and impactful concerns. But sometimes I think we tend to forget that there is still plenty of new physics to be found in the atmosphere. I love these kinds of problems; I have several papers in the works that might be considered pure (by which, ironically, i mean, applied) physics. I was reminded that there are still fundamental aspects of the atmospheric system which we do not understand that are rooted in simple physics by this interesting paper on the momentum budget of a mixture of air and condensing/evaporating rain drops — something most of us who think about clouds on a daily basis have never really considered. We’ve been studying atmospheric physics for over 100 years and yet there is still plenty we don’t understand. This is largely because we fail to translate physics we learned about in college to the atmosphere completely, not because the atmosphere is such a special place that it presents particularly unique physics.
I wanted to offer a couple of thoughts from my third Gordon Conference on Radiation and Climate. First off, I love the format. At a Gordon Conference, ~150 scientists are plopped down in a small (often, New England) college setting with nothing to do but talk about science 24 hours a day. It’s a fantastic opportunity for students, postdocs, and new and ‘experienced’ faculty to sit down and really discuss issues. Formal talks are 40 minutes and discussion lasts 20 minutes. It really allows for a deep-dive into complex topics. More than any other conference I attend, I always feel like I come away with a full appreciation for the major questions in clouds, radiation, and climate and what a lot of really smart people are doing to address them. I also come away motivated to pursue new questions and confident that we’re making progress on important issues. Second, I wanted to compliment the organizers of this particular GRC especially. It was very well thought out. The sessions and speakers were very well chosen. The point was repeatedly and well made that we need combined solutions to measurement and modeling problems. The time is past that these fields could be independent. I’ll see everybody again in two years.
Also, special thanks to those who offered Thursday night’s special surprise entertainment!
If there is a ‘part 1’, there was probably going to be a ‘part 2’, right? In the second paper up on my Publications page about the tropical precipitation pickup, I break apart the standard total column humidity metric. I show that most clouds don’t actually care what the column humidity is. In some senses that is not at all surprising, but in other senses that is. I separate the column moisture into two parts. Armed with these two new measurements of moisture, we can describe why the pickup value occurs where it does. The other interesting suggestion in this paper is that of a discrete set of rules cloudy atmospheres seem to follow. The moisture state of the convective tropical moisture is transient. At long enough time scales, the atmosphere is always moving toward the pickup value (either from below or above it). Local layers are also always trying to limit their local humidity. If you combine these two rules, you can compactly describe the evolution of the tropical convective atmosphere. Pretty cool.
It’s been a long time coming, but my first set of papers on the tropical “precipitation pickup” are finally in press. You can find a link to both papers on the “Publications” tab. In Part I, I spend ~6000 words discussing the ways in which clouds and precipitation processes depend on column humidity. It turns out that clouds don’t always care about column humidity. This makes sense for certain clouds like shallow/congestus convective clouds or for deep stratiform clouds that don’t span the troposphere. Yet, it has taken us nearly 15 years to actually put that down on paper. I won’t summarize the paper. If you want a quick summary, read the abstract. But, I will mention a few things that didn’t get published in that paper that I think are interesting. The first is that the precipitation pickup does not appear to be the result of a systematic change in stability. The tropical atmosphere is broadly unstable almost everywhere. Instability increases with column moisture, but it does not change state near the pickup in my simulations. The second is that nobody I have had a conversation with has agreed on precisely what the “scale invariance” of some of the precipitation statistics really means. If you, oh wise internet reader, have a suggestion, get in touch with me.
I was lucky enough to present some ongoing work at Bill Rossow’s retirement symposium earlier this week. It was a great chance to give an incredibly influential colleague (if I might be bold enough to call myself a colleague) a fun send off. Bill was a student of Carl Sagan’s at Cornell and got his start studying the clouds of Venus, Mars, and Jupiter. And, this is the thing that impressed me the most about Bill — his academic interests are truly eclectic. My feeling is that more of us should study a variety of things. It helps us makes connections that we couldn’t before. I think Bill’s work, especially his early work, bears the imprint of someone who was thinking about several different problems. Bill also taught me another valuable lesson: suspenders make the man. Happy retirement, Bill.