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.
It’s been a dry autumn here in Davis. We’re well behind the average rainfall for the water year. As I’ve discussed before, rainfall in the central valley in the dry season is very episodic. But what about rainfall in the transition from the dry to the moist season? Below are histograms of the first date of rainfall in a water year passing some threshold. X-axis is day of the year. Y-axis is a count of years. The threshold is listed in the title for each panel. For the lowest threshold, just 0.01″, the median first day of rainfall for the water year in Davis is September 25th (the ‘S25’ in the title). This year, my CoCoRaHS gauge saw 0.02″ of rain on October 2nd. So we were a little behind but well within the meaty part of the histogram. For a threshold of 0.10″, the median first date is October 5th. This is probably a more reasonable threshold for a real rain. In 2018, we have not yet seen a 0.10″ rainfall. Our first hope on the long range forecast is for rainfall in excess of 0.10″ on Thanksgiving day or day 326 of the year. That would put us well into the tail of that distribution. In fact it would be the third latest 0.10″ rainfall in the last 50 years. At higher thresholds, the distribution skews more toward later in the calendar year. For a 1″ threshold, 16 of the past 50 years have not seen rain before the end of the year (the ‘+26’ in the title). So, we’re behind for the year in accumulated rainfall, but not necessarily in our first big storm of the year. One big storm could get us right back on track and make our dry autumn unremarkable. That being said, I’m not holding my breath that the storm on Thanksgiving materializes.
The fine folks down at Florida State’s Meteorology program have put together a number of “academic family trees”. These are like normal, hereditary family trees except without your weird uncle. These trees link academics with their advisor (up the tree) and their students (down the tree). You can find the project here: http://moe.met.fsu.edu/familytree/ . There are a number of things that struck me (and forgive me if these things are written down elsewhere). First, in the ‘All’ tree there are two distinct branches before 1900 despite both branches being composed of Germanic academics. Second, it’s amazing the number of folks toward the end of the last century with a profound impact. Lance Bosart almost single handedly populated the far left side of the tree. Herbert Riehl, who founded my graduate program at CSU, effectively split the tree after WWII. He invented tropical meteorology and his branches have been pushing out from the middle ever since. If you take a look at the Cloud Physics tree and find me or Adele and begin to trace upward, you’ll quickly land on the Bill Cotton branch before heading further up. If you make it all the way up to one of the (current) tops of our line, you’ll find German physicist Georg Christoph Licthenber: https://en.wikipedia.org/wiki/Georg_Christoph_Lichtenberg . Adele’s professional middle name is Lichtenberger. It turns out Adele and I are academically related to Adele’s actual (many times removed) cousin. How cool is that?
OK, this isn’t so much a post as a statement of fact. The news loves to use the phrase that something “can be seen from space.” In 2018, that really isn’t such a hard threshold to cross. We’ll ignore the capabilities of non-civilian satellites for a moment and ask what can be seen from space from weather satellites. The MODIS instrument on board the Terra satellite has an effective resolution of of somewhere between 250m-1km. GOES-17 can resolve down to 500m. And the grand daddy of resolution, LandSat-8, can resolve images at 30m! Most of these resolutions are sufficient to effectively see most macroscopic terrestrial phenomena. At 30m resolution, LandSat-8 could, theoretically, see a house fire. So, let’s stop using the phrase when referring to objects/occurrences/phenomena that are clearly many, many times larger than these scales.
The thermometer at the UC Davis airport went a little crazy earlier today. It was reading a temperature of 171F. That’s clearly an error, but check out the heat index: 357F! That’s pretty meaningless.
I’ve never actually taken a look at the heat index equation, although it’s clearly some kind of empirical relationship. https://en.wikipedia.org/wiki/Heat_index . OK, so in temperature, it’s some constants, a linear term, and an squared term. It must be those squared terms that are doing the trick. Below, I plotted up the heat index between 80F and 175F (blue line) at 40% humidity (which apparently is the lowest limit for humidity in the equation???).
First, good thing it wasn’t 40% humidity or we would have had a heat index of over 500F earlier — makes 350F seem almost tolerable. Second, we see the issue with using empirical equations here. Empirical equations are often trained on easily measured data but applied beyond those ranges either unknowingly or by design. At these temperature scales, the red dashed line, a linear version of the full equation at low temperature, would probably be a better estimate of the true heat index. Also, just for comparison, the purple line is computed with a relative humidity of 80%. It’s over 1000F for a dry temperature of 175F.
The spring edition of the California CoCoRaHS newsletter (“California Cumulonimbus”) is up online. Check out my article on July rainfall in CA. The sneak preview is that there isn’t much…
A little delayed in posting this…
Warnings: 1) this is a joke; 2) figure’s linear fit may cause night terrors in mathematicians
Punxsutawney Phil puts up with a lot. Every year he wakes up from hibernation and makes a televised forecast. And every year, meteorologists and ivory tower academics (like me) reproach the little rodent for doing what he loves — predicting the early or late coming of spring. I think it’s time we all embrace Phil’. Not Phil the meteorologist, but Phil the climatologist. The figure below shows the years in which Phil predicts a long winter (a zero) or and early spring (a one). What you’ll notice is that Phil’s prediction of an early spring has become a lot more common since ~1980. I think we can all agree that this is clear evidence that Phil knows that climate change is real! Climate change is sometimes described as loading the dice toward warmer weather outcomes. Obviously Phil knows this; he is not flipping a fair coin. So, you heard it here first. Phil knows climate change is real. Long live Punxsutawney Phil, the oldest climatologist on the planet.
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!