Showing posts with label project folder. Show all posts
Showing posts with label project folder. Show all posts

19 January 2016

The Pacemaker of the Chandler Wobble

Abstract: The Chandler Wobble is one of the largest circumannual periodic or quasi-periodic variations in the earth's orientation.  After over a century of searching for its forcing, it was found to be caused by atmospheric circulation and induced ocean circulation and pressure.  The question of why there should be such forcing from the atmosphere has remained open. I suggest that variations in earth-sun distance cause this forcing to the atmosphere and thence the ocean.  Analysis of earth-sun distance, earth's orientation, and atmospheric winds shows a coherent relationship between the atmosphere and earth orientation at just those periods expected from earth-sun distance variation.  As this is a general mechanism, it can be used in examining regular climatic variations on a wide range of periods and for climate parameters other than the earth's orientation.

-- -- -- -- -- -- -- 

That is the abstract for the paper I link to below.  It's not a peer-reviewed paper in the sense of being in a peer-reviewed journal.   But it has been reviewed by an expert in the field (William P. O'Connor), who was quite favorable.

I am posting the idea and paper here.  Long past time for the ideas to be discussed.  If they're shredded in the blogosphere, so be it.  I have quite a bit more than what I've put in the document. Over the next few days and weeks, I'll post more of those additional materials as well.

The Pacemaker of the Chandler Wobble, Grumbine 2014

03 April 2015

Citizen Science Versus Science

It's impolitic to say so, but I dislike the term 'Citizen Science'.  Scientists are supposed to be embracing 'Citizen Science' and all that.  But I can't get rid of the feeling that it's a patronizing term.  Nor can I ignore the echo that scientists are something other than citizens.  Lose-lose.

The patronizing, maybe you don't see it.  But consider some other realms of activity.  I am, for instance, a runner.  Not a 'citizen runner', just a runner.  I have been in races with some people who were anywhere from slow beginners to world record holders.  In one race, I ran a 10 km against the (then) current men's marathon world record holder (Khalid Khannouchi) and the soon-to-be women's marathon world record holder (Catherine Ndereba).  No, I'm not great.  That's the point.  They ran their 10k, in about 28 and 30 minutes, respectively.  And I ran mine in about 45 minutes.  They were much better than I.  But we all (about 3000 of us) ran the same race, by the same rules, and were called the same thing -- runners.

Or consider music.  At one point, I played clarinet.  With tons of practice, I was able to get reasonably good results and sat near the top of my section in high school.  We were pretty good for a high school band, so maybe I was pretty good clarinetist back then.  The thing is, I know what seriously good musicians were like -- my sisters were both talented, one exceedingly so.  They were oboist and flautist.  The flautist might have been able to turn professional successfully.  Chose not to.  But you notice, again, same terms -- clarinetist, oboist, flautist -- used for us nonprofessionals as for the professionals. 

My take is, let's all go do science.  Not citizen science, just science, period.  Same as music or sports or anything else, some of us make a living at it, and many more will do it for the love of it.  But we're all engaging in the same activity, so let's also call it by the same name.  Same as we do for any other activity.

09 February 2015

The earth wobbles

The earth wobbles about in its rotation.  This was predicted long before it was observed, which is a story itself that I'll tell later.  For now, consider the earth and its rotation.  The north pole of the earth points towards the north star, and rotates once per day.  Open your right hand.  Your thumb points north, and when you close your fingers, they are moving in the direction of the earth's rotation.  With your arm making a right angle at the elbow, hand aiming away from your torso, you have an x-y coordinate system.  When you rotate your forearm, that moves your thumb in the x direction (positive or negative), when swing your arm forward/backward, that's the y direction.

The thing is, the earth (your thumb) doesn't always point in exactly the same direction.  There's a small bit of variation.  That's the wobble.  Since astronomers make their observations from the earth, it's very important to know exactly where the earth is pointing at any instant.  This lead (over 100 years ago) to the foundation of the IERS -- International Earth Rotation and Reference Systems Service.  Daily data from 1 January 1962 to (very nearly) the present are available at http://datacenter.iers.org/eop/-/somos/5Rgv/getTX/213/eopc04_08.62-now
Two things stand out to me in looking at this: There's a very slow tendency to increase x and y over time (increasing movement of the north rotational pole away from the original 0 point), and the more dramatic periodic variation.  The business of having slowly varying amplitude (size of the up and down) for the fast variations suggests a 'beat' is going on.  Namely, there are two different periodic variations going on.  When they're both at maximum, you get a large amplitude.  When they're at minimum, you've got a small amplitude.

07 January 2015

Edging towards a climatology

I say edging towards climatology because the process of going from here, a state of not really knowing what the climatology is, to there, a state of having pretty solid knowledge, isn't one I like to take in a single jump.  Even if scientists in professional journals present their work as if we did it in one jump, we seldom do it this way.  Plus, for our purposes here, it's more meaningful to proceed in successive approximations

For data, I'm going to use the Climate Forecast System Reanalysis (v2).  I'll also be using the high resolution, in time and space, versions of the data.  This leads to some pretty big files (unpacked, it is about 2 Gb per month, and remember there'll be 360 months for a 30 year climatology).  So you might want to go with the lower resolution for your own initial exploration.

To start with, let's look at the 2 meter air temperature, where I've converted temperatures to Celsius (from Kelvin).  30 C = 86 F, 0 C = 32 F.  The total planetary range is a bit over 70 C from the very coldest areas (Antarctic Plateau -- below -40 C) to the warmest (pretty much the whole tropics).

02 December 2014

Girls on Ice 2015 Expeditions

A chance to go climb and sleep on glaciers in either Alaska or Washington State, plus learn and do science.

Supported in part by the NSF and Alaska Climate Science Center.
Application window opens 10 December 2014, closes 31 January 2015.

From Girls on Ice Web Site:

Girls on Ice is a unique, FREE, wilderness science education program for high school girls. Each year two teams of 9 teenage girls and 3 instructors spend 12 days exploring and learning about mountain glaciers and the alpine landscape through scientific field studies with professional glaciologists, ecologists, artists, and mountaineers. One team explores Mount Baker, an ice-covered volcano in the North Cascades of Washington State. The other team sleeps under the midnight sun exploring an Alaskan glacier.

“Girls on Ice is not a reward for past good grades or academic achievement, it is an inspiration for future success.”

The application period for the 2015 Girls on Ice Teams will begin December 10, 2014 and end on January 31, 2015 at 11:59 p.m. Alaska time.

Alaska program: June 19 – 30, 2015

North Cascades program: July 13 – 24, 2015

To be eligible, girls must be at least 16 years old by June 19, and no older than 18 on July 24.


01 December 2014

High School Educational Program on Greenland

For US High School Students -- a chance to work in Greenland doing science.  Application deadline 9 January 2015

More information, including the application, is available at:
http://www.arcus.org/jsep

(From the web site:)


In this successful summer science and culture opportunity, students and teachers from the United States, Denmark, and Greenland come together to learn about the research conducted in Greenland and the logistics involved in supporting the research. They conduct experiments first-hand and participate in inquiry-based educational activities.
The JSEP format has evolved over the years into its current state, which consists of two field-based subprograms on-site in Greenland: the Greenland-led Kangerlussuaq Science Field School and the U.S.-led Science Education Week.
Program Dates and Descriptions
Kangerlussuaq Field School (2 weeks) and Science Education Week (1 week): Tentative dates for JSEP 2015 are June 29th through July 20th.
Kangerlussuaq Science Field School: Students learn about and participate in polar science alongside researchers and teachers at field stations around Kangerlussuaq, Greenland. This area is a rural region with limited amenities. Participants live in dormitory style housing and share in cooking and cleaning responsibilities. This part of the JSEP Program is supported by the government of Greenland.

28 August 2014

Exploring Arctic Ice Minima

Every year 2007-2013 had a lower Arctic sea ice extent than every year before 2007.  2014 seems likely to continue this record.  I'll also suggest below that maybe the Arctic has entered a 'new normal', with September ice extents bouncing around 4.7 million km^2. 

For some data to work with further, I pulled the NSIDC September figures.  It's a small, simple text file, so you can check yourself what follows.  First up, let's draw a figure of what we're looking at -- but don't connect the observation dots.  Our eyes tend to be led to conclusions by the superposed lines.
You can check some of the sources for ice before 1979 and see that figures below 5.5 million km^2 are unprecedented in the longer records as well.  To have data precision and consistency, though, I'll stay with the 1979-present.

What else can we say from eyeballing the data?  Since the 1979 starting point:
  • There have been 2 record highs (1980 and 1996)
  • There have been 8 record lows (1984, 1985, 1990, 1995, 2002, 2005, 2007, 2012)
  • There have been more record lows in the last 10 years (3) than record highs in the full 35 year record
  • 1996 is about the last year one could say there was no trend in the data
  • Versus eyeball curve fitting, 1996 is the most exceptionally high year (not just an absolute record, but even higher above smooth curves we'd try to fit to the data than any other year).
  • More recent years look like they have more scatter than the earlier years
  • It looks like we might want to divide the period in to 3 intervals -- 1979-1996 (the longest arguably trendless span), 1997-2006 (an intermediate with at least some overlap on the earlier figures) and 2007-present (entirely outside the range of the previous years)
But maybe your eyeballs disagree with mine, and perhaps the appearances are deceiving.  So on to working with numbers, which will also lead us to some additional ideas.

28 July 2014

Yabba2 -- Construction


Katherine Monroe:

Below are the full instructions on how to build exactly what I built. There is so much that could be done to improve the design. I know it is not anywhere close to perfect. The materials I used were makeshift, whatever was lying around the house or wasn’t too expensive. But that was the point. I like spontaneity. It doesn’t have to be extremely elaborate to work and to be useful. This is for anyone who wants to do anything with it or for anyone who is just interested.
  
Materials
1. Vernier Flow Rate Sensor, Order Code: FLO-BTA/FLO-CBL
2. Vernier Lab Quest by Vernier Software and Technology.13979 SW Millikan Way, Beaverton, Or 97005. 888-837-6437. (for transmitting and collecting data from the Flow Rate Sensor.)
3. 3 22” steel dowel rods
4. Compressed fiber board
5. Minwax Polyurethane Varnish
6. 24 Gauge- 100 ft. Green Floral Wire Twister
7. Small foosball
8. 2 IDEC Sensors, Magnetic Proximity Switches. Type: DPRI-019. Premium Waterproof Clear Silicone Sealant (without Acetic Acid)
10. Plugable USB to RS-232 DB9 Serial Adapter (Prolific PL2303HX Rev D Chipset)
11. RS232 Breakout - DB9 Female to Terminal Block Adapter
12. Xnote stop watch, version 1.66 (downloadable at http://www.xnotestopwatch.com/)
13. Loctite Epoxy glue
14. Drill
15. Hammer
16. 2 Brass quarter inch Phillips Head screws
17. Electric hand held reciprocating saw
18. Electrical tape
19. 4” by 3/4” strip of thin steel (cut from a can)
20. Twisted Nylon string
21. 2’ long wooden slat (to be used as a handle for carrying and placing the designed device in the water.)
22. Study Site: United States Geological Survey (0164900), Northeast Branch of Anacostia River at Riverdale, MD. (Test site was just next to the USGS data collection gauge.) (-38.961,  -76.626)

25 July 2014

Yabba -- Building your own stream gauge

Katherine Monroe*, the author/inventor of this stream gauge, is a graduate of Eleanor Roosevelt High School, in the same class as Elliott Rebello.  Her senior project was quite different, and you'll get to see the details in her own words.  Part 1 is today, the narrative.  Part 2 will be on Monday -- the full parts list and construction instructions.


Engineering the “Yabba Dabba Doo”

By: Katherine Monroe
June 2014
Eleanor Roosevelt High School

One year ago, as a rising senior at Eleanor Roosevelt High School in Greenbelt, MD, I was faced with the same grueling task that all students in the Science and Technology program were: RP- that is Research Practicum. This is what we had been leading up to for the past three years and now, here it was. 

RP is the year long research project that all seniors in the Science and Technology program at Roosevelt are required to complete. By the end of the year we had to have completed a science fair backboard, a laminated poster, a power point, and a five chapter paper. We had a whole class dedicated to working on all the different aspects of the project and to learning how to analyze data quantitatively and statistically. We were told to come up with a project that was interesting to us because we would be spending the entire year working on it. Some students applied for internships with NASA, USDA, NIH, the University of Maryland, the National Zoo, Walter Reed Hospital and more. Other students applied for programs established by and within the school and other students worked separately from any structured programs. 

I chose to apply to a program started by one of our school’s AP Chemistry teachers called WISP (Watershed Integrated Study Program.) It was a program which emphasized local water quality studies. Students in WISP formed groups and measured chemical and physical properties of local waterways at a bunch of sites across the county. We measured nitrate and phosphate concentrations, dissolved oxygen levels, alkalinity, ph, turbidity, total dissolved solids, temperature and took seasonal macroinvertebrate data. We then added our values to an ongoing database which students could draw from for all sorts of studies which require long term data collection.

I applied to WISP because out of the endless ocean of things I was unsure of I was sure of at least one thing and that was my love for the environment and for being outdoors. After having been accepted to WISP I began the process of deciding what to do for my project. In the end the basis of my project came from the one other thing I was sure of which was that I enjoyed building things. So I knew I wanted to build something and I knew it should relate to the local water quality movement that WISP was promoting. I looked at what we did in WISP and thought about what we measured. One aspect of water quality that I found important to a gaining a comprehensive understanding of a stream or river’s health (that we did not measure in WISP) was the speed of the water in the stream.

The water speed can provide insight into the types of organisms that can live in a stream or river, to the flow of sediment down a river, and sometimes to the oxygen levels of a river. The greater the speed of a river, the more aerated it typically is, and the higher the dissolved oxygen level. All of these can greatly affect the health of a stream or river. Stream speed can also help in understanding volume flow rate of a stream and in identifying storm water runoff patterns near and around the stream or river and in developing flood models. Overall stream speed seemed like an important factor that we did not account for in WISP due to what I believe to be a range of reasons, the expense of the necessary equipment, the complicated nature of taking stream speed measurements at a variety of points along a stream and still getting inaccurate results due to the variability of speed along an uneven stream bed, and maybe more. 
 
I decided that I wanted to design and build something that would measure stream speed; something that would be cost effective and accurate, and something that would be easy for anyone who wanted to do research, like the kind we do in WISP, to build for their own purposes. The point was to encourage citizen science by going through all the steps independently and then showing people what I had done so that they could do it too. 
 
In the end what I came up with consisted of an open track along which a light and neutrally buoyant ball was pushed by the flowing water. On either end of the track there were magnetic sensors which timed how long it took for the ball to move from one end of the track to the other. From this the speed was computed. This is where the name of the device comes in. I decided to call it the “Yabba Dabba Doo” because it looked like something out of the Flintstones (or maybe like an old-fashioned push lawn mower.) 
 
Next I had to figure out if my design actually worked. In order to do that, I compared my device to an already existing speed measurement device by the company Vernier. I assumed that the Vernier data was accurate. My null hypothesis was that the average of the speeds taken with my device would be statistically equivalent to the average of the data taken with the Vernier device. Strangely enough, I wanted to FAIL to reject the null hypothesis. Statistics are weird. I collected data with each of the devices within a half an hour period of each other (assuming that the stream speed would not change in that amount of time.) Then I analyzed the data through a statistical t-test which looked for a significant difference between the two sets of speeds and their averages. 
 
After multiple trials and readjustments to the design I got what looked like a pretty accurate result. Initially (before reaching my final design), the object moving along the track of the Yabba Dabba Doo was a metal disk attached to the metal rods of the track with metal rings. All that metal caused for a lot of friction between the disk and the track which prevented the disk from reaching the speed of the water and gave me slower averages than the Vernier averages. This also yielded a significant difference in the statistics which I did not want. In order to minimize the coefficient of friction there, I changed my design to one which consisted of that light weight, neutrally buoyant foosball (which I mentioned earlier,) that was attached to the metal track with small sections of plastic drinking straw. The foosball had no tendency to float or sink in the water and caused less friction on the track. Furthermore, the coefficient of friction between the plastic straw and the metal track was much less than between the original metal rings and the metal track. After making this design change I got averages that were much closer together between the Yabba Dabba Doo and the Vernier and in a majority of my statistical t-tests there was no significant difference. In the end I had a device that seemed to be working pretty accurately and cost $350 less to build than to buy the Vernier. 
 
Going through that process, of trial and error and trial and error and trial and then success!!! was extremely gratifying. I got to experience the life of an engineer first hand and to learn about the plethora of unforeseen problems that can arise. 
 
This entire year was a great learning experience for me. I learned what a null hypothesis was and how to go about trying to reject it (or in my case, fail to reject it.) I learned all sorts of things about the materials that I used to build my device. I learned how to bear standing out in winter weather water up to my waist, wearing my mom’s baptismal waders, for the good of science! I learned about all the things that can go wrong and need to be accounted for in a field study like this one. I learned how to access all sorts of functions on excel, power point, and word. And I learned something about myself. I learned that engineering, and I think in particular environmental engineering, is something that I could easily be passionate about and be satisfied with in the future. And for a chronically confused and disoriented teenager about to go to college, that is reassuring.

Below are the full instructions on how to build exactly what I built. There is so much that could be done to improve the design. I know it is not anywhere close to perfect. The materials I used were makeshift, whatever was lying around the house or wasn’t too expensive. But that was the point. I like spontaneity. It doesn’t have to be extremely elaborate to work and to be useful. This is for anyone who wants to do anything with it or for anyone who is just interested. 


[Back to your host: Directions on Monday ; The * is that Ms. Monroe normally goes by a more informal version of her name and I've gone with the formal here.  Formal for publication is a rule I use myself (I'm not usually Robert), and one which I've learned is helpful for women to be taken seriously.] 

28 January 2014

Old links still of interest

I'm something of a pack rat -- keeping things eternally, or close to it.  I still have, for instance, almost every program I wrote in college, plus almost everything ever since.  I also have preserved links of interest from my blog reading back to ... well, not quite that long.  Part of my getting back up to speed is to look through my old noted links of interest, and I'll share them out.  I'm more or less arbitrarily diving them in to links of interest, and links to follow up.  Everything actually fits in both categories, but a bit of a matter of emphasis between them.  

The items to follow up are old enough that you and I can do some searching to see how well they've held up over time.  The papers are interesting and good, but many interesting and good papers turn out not to stand up without important additions or modifications over the next few years.  These links are at least 3, and some over 4, years old, so there's been some time to see the evolution of thinking the the fields.  Some papers' conclusions get stronger over time, some weaker.  Pick a topic you're interested in and see what happened through time.

I'm also noting twitter identities for the blogs/bloggers I link to.  I'll post a separate note tomorrow asking for your suggestions.

 
Items of interest

Open Source Climate Education
TB is also on twitter at 

FAQ on climate models -- part 1
FAQ on climate models -- part 2


Science is something people do

computing before electronic computers

A Google Earth Explosion! -- Geology layers for Google Earth
Kim is on twitter at

 Dan Moutal: Why I accept the scientific consensus on global warming, and what would change my mind
-- I also have a number of comments there.
Dan is on twitter at and

Naomi Oreskes video: American Denial of Global Warming


Items to follow up to see how well they've held up over time:

Sea Level may rise 1 meter by 2100
21 Meter sea level rise 400,000 years ago
West Antarctic Ice Sheet and sea level last 5 million years

Reconstructing and estimating sea level 200 to 2100 AD
You can also follow Aslak on twitter at

New studies disprove cosmic ray driver for climate
Post 1850 global temperature increase not driven by sun
IPY sea ice model -- Arctic Ice probably will not recover
Coastal Erosion doubles in parts of Alaska

16 September 2013

Where is south?

You, too, can be a space alien!  All you'll need are a stick, some sun, string, and a way of making marks.  At the end of this, you'll be able to perform feats that have caused many over the decades to say that the ancient Egyptians and others 'must' have been visited by space aliens.

What you'll do is construct a very accurate definition for north+south.  From there, you can build your own pyramid aligned accurately to north/south.  Technologies involved are all 6000+ years old.

So, first step is to get a long, straight stick.  You can verify that it's straight by checking against the string in your which way is up apparatus.  (Notice that it, too, only uses 6000+ year old technology.)

Next, put the stick in to an area of flat ground.  It's best if you plant it straight up and down.  You can use your terribly advanced up-down apparatus for guidance on which way is up.

Now for the hard part.  Tie the string around the stick and your marker.  Pull the string taut and mark a circle around your stick.  Retie the string to marker and shorten or lengthen the distance from stick, to make another circle.  Repeat a few times so that you have a variety of circle sizes.

Back to easy.  A little patience is required.  Each time the sun's shadow from your stick hits one of the circles, mark the location.  If you're in a cloudy place, you really want a bunch of circles.  What we're looking for is the shadow to hit the same circle twice -- once earlier in the day and once later.

Next to last: after you have a nice pair of marks on one of your circles, find the mid-point between the pair.

Finally: Draw a straight line from your stick to this mid-point mark.  This line is the north-south line for your location.

There are some elaborations you can do here, for accuracy and for large scale construction.  But you're now done with the basics.  And can construct your own objects aligned accurately to north-south, just like the 'space aliens'.  Well, more seriously, just like our ancestors from several thousand years ago.

14 August 2013

Bits and pieces

Somehow, even though I no longer am in school, or have kids of my own who are, I'm still pretty distracted from the net around back to school time.  Maybe it's my nieces and their back to school time?

Anyhow, many things going on, though you couldn't tell from the blog here.  And some even have school connections.  One part is, I've been talking to a middle school science teacher, J, about ideas, J's and mine, for grades 6-8.  Of course I mentioned my water surface temperature project.  Also some more specific ones.

I've also been thinking of blogging some experiments that people and classes could do about the earth and eventually climate.  Start with determining the size of the earth following Eratosthenes method, earth's rotation rate (which is not 24 hours per day), and the sun's motion (which also isn't 24 hours per day). What ideas do you have?

Thursday and Friday, I'll be at ScienceOnline Climate, in Washington DC.  It's also on twitter #ScioClimate and I've been more on twitter myself lately (@rgrumbine).  A post relating to that is Liz Neely's What the Science Tells us about Trust in Science.  (Problems there with comment section, so my brilliant comment vanished in to the ether.)  Via twitter, @dougmcneal started collecting observations on why people might distrust a climate scientist.  I've added in a few I've encountered first hand.  I'll suggest you add yours -- that you feel yourself, or that someone has said directly to you.

25 July 2013

Interesting numbers

There's a sort-of theorem in mathematics that all numbers are interesting.*  But I'm thinking first of 1729, which is the subject of a story about how it is an interesting number.  The story is that Ramanujan, a brilliant, self-taught mathematician, was in the hospital (he died at only 32).  G. H. Hardy visited him and commented that his taxi cab was number 1729, which wasn't a very interesting number.  Ramanujan replied that it was indeed interesting -- it was the smallest number that was the sum of two cubes, in two different ways.  That is, 10^3 + 9^3 = 12^3 + 1^3 = 1729.  This number appeared also on a cab in an episode of the Simpsons.

There's a different bit of playing with numbers, one of the longest-unproved theorems in mathematical history.  That is Fermat's Last Theorem.  (Itself misnamed, as he never showed a proof for it, and he worked for years after stating it.)  That is, if we use only integers (1,2,3,...), the equation x^n + y^n = z^n has no solutions for n > 2.  He said this in 1637, and it wasn't proven until 1995.

Let's look specifically at n = 3.  I can rewrite Ramanujan's example as:
x^3 + y^3 = z^3 + 1  (where he had x,y,z = 10, 9, 12)
Fermat's equation is:
x^3 + y^3 = z^3   -- and this has no solutions for integers.

That's interesting -- such a small change, and we go from having no solutions, no matter how large we make x,y,z, to having ... how many?  Well, that's a question.  My version here is a more specialized version of so-called 'taxicab numbers' (named in honor of the above story).  You can see some more about them at Durango Bill's.  But I like mine better because of the connection to Fermat's Last Theorem. 

It seems common that answers in mathematics are either 0, 1, or infinity.  Fermat's equation (for n > 2) has 0 solutions.  We already have 1 for my 'Fermat-Ramanujan' equation.  If there's another, not that this is a proof, probably there are an infinity.  So I set my computer to some brute-force searching, and indeed there are more.  Not many.  It found 92 for z going from 12 (the smallest that has a solution) to 2,000,000 (which was pushing the limit of the computer; z^3 at that point is 8,000,000,000,000,000,000).  That suggests that there are an infinity of solutions to the Fermat-Ramanujan equation (a name I just invented, as far as I know), by that rule of thumb.

Challenges:
Can you find some? 
Can you do it by a more elegant method than having a computer pound away?
Can you prove that there _are_ (or are _not_) an infinity of solutions?

30 May 2013

The world is very small

We all run in to plenty of situations which make us think or notice that the world is a very small place. Last week gave me yet another example, though in a bit I'll be challenging you to do some examination to see just how surprising it really is.

 The smallness of the world comes from this photo of Genevieve (Jenny to family) Ramsey getting her Master of Fine Arts from Queens University in Charlotte, North Carolina last week. I was down for my wife's graduation from the program, but ran in to Genevieve, who's looking (my wife said) a decade younger than she had in January.
Small world aspect: I mentioned my interest in weather/climate, and she mentioned her nephew and his father who also are. Turns out one is Steve Skolnik (I guess this is the father), who is also proprietor of Capital Climate, a blog to which I link over in the blogroll, and whom I've met in 3d.

Do keep an eye out for Genevieve's memoirs when they get published.  The first book includes autobiography, and her fight with cancer (hence looking so much better now).

The challenge aspect:

01 May 2013

Assessing forecasts

This is actually part of pursuing whether ESMR was screwy, but I decided that to show that nothing was up my sleeve, it was time to talk some about assessing forecasts.  That, and it's something I've been meaning to talk about for a while.  The thing is, forecast assessment is not nearly as simple as we sometimes think.  Having judged many a science fair project that is comparing weather forecasts, I've seen many of the same issues come up there, too.

For precipitation forecasts, people (science fairs included) often think about either 'probability of detection' -- i.e., what fraction of the time that there's rain did the weather forecast call for rain, and 'false alarm rate' -- what fraction of the time did you get no rain even though the forecast called for rain.  Both are potentially meaningful, and both have serious problems if used alone.

22 April 2013

Forecast Contests

I'll invite your suggestions for forecast contests to hold.  In the mean time, some results from forecast contests at my work. 

The winter contest was to predict the date of the first 2 inch (5 cm) snowfall at our official weather station.  It never happened.  I came close to predicting the date, sort of.  Since we've had some memorable storms on or near President's day (February 18th this year), I went with that.  Nothing noteworthy that day this year.  But the next guesser was for May 1, so when we were getting forecasts of significant snow (4-8 inches, 10-20 cm) in mid-March, I was hopeful.  Only 1.7" at the official station, though, so no luck for me.  (If only we'd used any of the other area stations!  All beat 2".)  This is our second straight year of not having even one day with 2" of snow.  Should probably adjust the standards to 1" (2.5 cm).

The summer contest had a winner before entries had even closed.  One of the contests was to predict the first day that the official station would exceed 90 F (32 C).  Entries open to the 30th of April, it happened the 10th if I remember correctly.  The 7th earliest date ever.  Spring here, apparently was April 8th and 9th.  We're now on summer.  Note to future: have to close entries on the summer forecast contest on April 1st or earlier.  (Our earliest ever 90 F day was apparently late March -- 27th, iirc).

Both contests suggest that traditional weather forecast contests need some updating for changing climate.

For here, a couple of contests that came to mind, in addition to the 'traditional' guessing of the September average Arctic sea ice extent, are to guess when the atmospheric CO2 levels for Mauna Loa monthly average will pass 400 ppm, and when it will pass 150% of pre-industrial (420 ppm).  One that can be done annually, guess the first week when Arctic sea ice extent will fall below the climatological (1979-2000) minimum extent, and guess how many weeks the ice will remain below that minimum.

Other ideas?

01 April 2013

When was climate normal?

It's been a couple years since I took up the question of normal climate, so time for another go.  At that time, I used monthly data from Hadley, and arrived at the observation that if you're younger than 26, you've never seen a month where the global average as as cold as the 1850-2011 average, 317 consecutive months (at that point, now over 330) of warmer than 'normal' temperatures.  I'll cheat and give you some answers first, read on to see how they're established:
  • Climate was 'normal' only between 1936-1977
  • Every year 1987-present has been warmer than any year before that
  • 1976 was warmer than any year before 1926
  • 1978 (next coldest year of the recent run) was warmer than any year before 1940
Do read on to see what 'normal' winds up meaning; it's important!  One part of 'normal', as we intuitively think about it, is that you should some times above it, and sometimes below.  Having many consecutive years above 'normal' says that normal isn't really very good.  To help get quantitative about how to proceed, consider this plot of NCDC's data (warmer/colder than the 1880-2012 average -- the length of the entire record).


07 February 2013

Time to go do some science

NOAA/NWS/NCEP/Environmental Modeling Center has a request for data out, one which gives anyone near water who can read a thermometer a chance to do some science.  There's a science history behind why this is a request and I'll give my own, biased, view of that.

All data have errors and are messy.  Though George Box's comment is often repeated at modelers ("All models are wrong, some models are useful.") it is equally applicable to data and data analysis.  All data are wrong, some data are useful.

In the case of sea surface temperatures (sst), efforts to analyze global ocean sst started with badly distributed data sources -- ships.  They give you a fair idea of what the temperature is along the paths the ships take.  So the ship route between New York and London is pretty well-observed by ship and has been for a long time.  But not many ships go through the south Pacific towards Antarctica.  If you want to know what's happening down there, you need a different data source.  One such is buoys.  Though, again, buoys are distributed in a biased way, being mostly towards shore (so that they can be maintained and repaired).

Then came satellites and all was good, eventually, for a while.  Polar orbiting satellites see the entire globe.  Starting with instruments launched in the early 1980s, it has been possible to make pretty good analyses of global sst, at least on grid cells 50-200 km on a side.  Since that is as good or better than any of the ship+buoy analyses could do, that was a great triumph.  The ship and buoy data, though, remained and remains important.  One of the problems that satellite information faces is that the instruments can 'drift', that is, read progressively too warm, or too cold.  To counter that possibility and other issues, the surface data (in situ data) is used as a reference.  So for a time in, say, the early 2000s, all was good.

But both scientists and users of scientific information are never satisfied for long.  For sst, some of the users are fishermen -- some fish have very particular temperature preferences.  As it became possible to do a pretty good global 50 km analysis, with new data over about 2/3rds of the ocean every day, scientists and users started demanding more frequent updates of information, and on a finer grid.  They also got increasingly annoyed about the parts of the ocean that only got new observations every 5-20 days.  This includes areas like the Gulf Stream, where it is often cloudy for extended periods.  The traditional satellites are great, but don't see through cloud.

Another major user of sst information is numerical weather prediction.  When weather models were using cells 80-200 km on a side, the sst at 100 km (say) was a pretty good match.  But weather models continued to push to higher resolution, so that by the early 2000s, 10 km grids weren't unheard of.  The reason for such small grid spacing in weather prediction models is that weather 'cares' about events at very small scale.  If weather cares about those smaller scales, then it become important to provide information about sst at the smaller scales.  An inadvertent proof of that was made when a model made a bad forecast for a December 2000 storm, and the cause was traced back to an sst analysis that was too coarse.  See Thiebaux and others, 2003 for the full analysis.

Plus, of course, there is interesting oceanography that requires much finer scale observations than 100 km.  So a couple of different efforts developed. One was to use microwave data to derive sea surface temperatures.  AMSR-E was the first microwave instrument used for sst in operations, as far as I know.  (Sea ice isn't the only thing that you can see with microwaves!) That addressed the issue of seeing the Gulf Stream (and other cloudy areas) most days.  The other was to start pushing for higher resolution sst analysis. This lead to an international effort to analyze the global ocean at high (say 25 km and finer, sometimes 10 km and finer) grid spacing.  More is involved in that than just changing a parameter in the program.  (You'll get an answer if you do that, but it won't be as good as you had at the coarser grid spacing).

On the ocean side, this quality of the high resolution analyses is doing relatively well.  But as you go to finer grid spacings, new matters appear.  The Great Lakes are very large, so they can be seen by satellite easily, and they have buoy data through at least part of the year, so that the satellite observations can be corrected at need.  But ... go to a finer grid spacing weather model and you discover that there are a lot of lakes smaller than the Great Lakes.  For a 4 km model, there are some thousands of lakes just in North America.  And none of them have buoys, and almost none even have climatologies.  Also at this grid spacing, you start seeing the wider parts of rivers.

Here's where an opportunity arises for people who live near a shore (whether river, lake, or ocean).  NOAA/NWS/NCEP/Environmental Modeling Center is requesting observations of water surface temperatures to use as a check on their analysis of temperatures in areas close to shore.  (close equals, say, up to 50 km (30 miles), and at least 400m (quarter mile) from shore). Check out the project's web page at Near Shore Lake Project

As always, I don't speak for my employer or any groups I might be a member of. I'm pretty certain that all people who work on sst would disagree with at least parts of my above mini-history.  Be that as it may, it should be a fun project.

31 January 2013

Sea level's climate time scale

My 'reality-based decision making' post prompted a comment asking for my thoughts about sea level rise, which is more than sufficient excuse to turn to that.  An additional excuse is that it provides a chance to look at how to decide climate time scales for something other than temperatures.  For global mean temperature trends, I found that you need 20-30 years to determine a climate trend.  We'll see that it is 40-60 years, 60 for preference, for sea level.

My starting point for data was the University of Colorado sea level group.  They provide satellite data back to late 1992.  High quality data, but only for a short period of time.  If global sea level's time scales are like global mean temperature's, then it's only just gotten long enough to provide a climate number.  Fortunately they list links to other sea level groups, including the Permanent Service for Mean Sea Level.  They have three global reconstructions available.  I'll take this one -- published in the scientific literature as: Recent global sea level acceleration started over 200 years ago?", Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth (2008), Geophys. Res. Lett., 35, L08715, doi:10.1029/2008GL033611 -- on the grounds that it covers the longest time period and has the most recent literature publication date.  It will be a good project for a reader to see if the conclusions here change, and how, if you use one of the others instead.

06 July 2012

Century storms

"That's two straight years we've had a 'storm of the century'; those weather guys are idiots!"  Not long after I moved to the Washington, DC area, this happened, and the quote is real.  I'm not sure that the storms involved really were 'storm of the century' events -- events that if we had a long enough record, we'd see happen about 10 times per 1000 years -- but it's something to think of a little quantitatively, particularly in light of my normally abnormal note.

Let's suppose that we're building a house and would like it to last 30 years.  Well, to be specific, let's say we'd like a 99% chance of it lasting that long.  Obviously it has to be able to survive events that we'd expect to happen once per year.  And we can probably ignore things that we'd expect only once in a million years.  But what about a once in 100 year event?  The name misleads us in to thinking that the next time such an event would happen is 100 years after the last time.  While natural reading, it's wrong mathematics.  We could easily be in the unlucky 30 years that sees a 100 year event.  We could even see it twice.  But is there less than a 1% chance of having one 100 year event in a span of 30 years?  That's our design requirement.  If it can be expected more often than that, our house design is not reliable enough.  We need something better. And we'll need to get quantitative.