On the history of the AMS AOFD conference

In an earlier post celebrating the 100th anniversary of the ASME Fluids Engineering Division, I expressed some confusion about the history of the American Meteorological Society's Atmospheric and Oceanic Fluid Dynamics Conference.  I wondered if it had not always been called by that name.

Indeed, the first Conference on Atmospheric and Oceanic Waves and Stability was held in 1976 in Seattle, WA; with the second in 1978, held in Boston, MA.  The 10th was held in Big Sky, Montana in 1995. For the 11th conference in 1997 in Tacoma, WA, the name changed to the Conference on Atmospheric and Oceanic Fluid Dynamics, a name that has stuck ever since.  I'm not sure at what point the meeting became annual rather than every 2 years.

Anyway, the conference is 50 years old this year, another celebration worthy of note!

 

 

Congratulations to the ASME Fluids Engineering Division on their 100th anniversary

Earlier this month, I learned that the Fluids Engineering Division (FED) of the American Society of Mechanical Engineers (ASME) is celebrating its 100th anniversary this summer at their annual summer meeting (FEDSM2026).  They may be the oldest fluid mechanics-specific professional society in the United States...let's take a look at the evidence for this claim.

First, ASME was founded in 1880, and its Hydraulics Division (HYD) was formed in 1926, with Prof. Lewis F. Moody as a prime mover in its creation.  Its first separate division conference was held in 1961.  Prof. Robert C. Dean led the effort to change its name to the Fluids Engineering Division (FED) which succeeded in 1963.  A restructuring occurred in 1989-1990, but the name remained.  There was also a Committee on General Hydraulics which existed between 1938-1940, led by Murrough P. O'Brien, and later a Fluid Mechanics Committee, formed in 1957 by Dean, now called the Fluid Mechanics Technical Committee (FMTC).  The Journal of Fluids Engineering was founded in 1973, also under Dean, its first editor.  I've pulled this information from the article by Cooper et al. (2016).

We must turn now to the American Society of Civil Engineers (ASCE), founded much earlier (1852).  Its Hydraulics Division was formed in 1938, the 12th such division formed.  Fred C. Scobey of USDA was the prime mover of that effort.  Its first specialty conference was held in 1950, and its Journal of the Hydraulics Division began in 1956.  In 1982, the journal was renamed the Journal of Hydraulic Engineering.  A 50th anniversary book for the division was published in 1990, but I have not been able to see a copy.  My information is drawn mainly from Petersen (2002).  It is unclear to me from the society's website whether the Hydraulics Division, or its specialty conference, still exist, but the journal definitely does.

The American Physical Society (APS, founded in 1899) created its Division of Fluid Dynamics (DFD) in 1947, its fourth division.  Raymond J. Seeger was the prime mover of this effort.  The first annual meeting was held in 1949, though sessions were sponsored at the 1948 APS annual meeting, including a joint meeting with the Institute of Aeronautical Sciences (a predecessor to the AIAA, about which more later).  However the APS did not start a special fluid mechanics journal until 2015, Physical Review Fluids.  Prior to that, the DFD collaborated with the American Institute of Physics (AIP) in the publication of Physics of Fluids, founded in 1959 under Francois Frenkiel.  Some historical information on the DFD can be found in this piece by Russell Donnelly.

For the remainder of this post, information is drawn from Wikipedia and/or the websites of the professional societies themselves.

The American Institute of Aeronautics and Astronautics (AIAA) was formed in 1963 from the merger of the Institute of the Aerospace Sciences (founded in 1932 under the name Institute of Aeronautical Sciences, mentioned above), and the American Rocket Society, founded in 1930.  At one time the AIAA sponsored a Fluid Dynamics Conference, but it does not seem to exist anymore.  They do have a Fluid Dynamics Technical Committee, within their Aerospace Sciences Group.  I do not know when it was formed, but it can't be older than AIAA itself.  AIAA does not seem to have a specialty journal for fluids; rather fluids papers appear across the constellation of their journals on other subjects.

The same about the journals can be said about the American Institute for Chemical Engineering (AIChE), founded in 1908.  They don't seem to have a specialty journal, conference, or division or forum dedicated to fluids, as the topic basically pervades much of the discipline, as it does for AIAA.

Indeed, many professional societies in science, mathematics, engineering, and the Earth, environmental, and planetary sciences make use of fluid mechanics, and many of their practitioners carry out fundamental research in fluids.  Fundamental and applied topics in fluids research pervade across multiple technical areas within these professions, so often they do not have a fluids-specific unit, journal, or conference.  Examples include the Society for Industrial and Applied Mathematics (SIAM), the American Geophysical Union (AGU), and the American Meteorological Society (AMS).  However, the AMS does host an annual Atmospheric and Oceanic Fluid Dynamics (AOFD) conference.  It has not always been annual (the most recent, just held earlier this week, was called the 25th, but I coauthored a presentation given at the 13th in 2001), so I'm not sure when it began.  I have seen an announcement for the 2d AMS Atmospheric and Oceanic Waves and Stability conference in 1978, so perhaps this is the same conference under a different historical name?

So to conclude, the major U.S. professional society units that specialize in fluid mechanics appear to be the ASME's FED and the APS's DFD, both of which sponsor annual conferences and journals (JFE and PR Fluids).  The AIAA has a technical committee but no special conference or journal.  The AMS also has their AOFD annual conference, and the ASCE maintains the Journal of Hydraulic Engineering. To these we can add the AIP's Physics of Fluids journal.

The surviving units, ASME FED and APS DFD, were created in 1926 and 1947 respectively, so indeed FED is the oldest that I know of in the United States.  The ASCE has the oldest journal discussed here, however, since JHE dates to 1956, with PoF following in 1959, JFE in 1973, and PR Fluids in 2015.  Finally the oldest surviving conference discussed here is DFD's, starting in 1949, followed by FEDSM in 1961, and AOFD possibly dating to sometime in the 1970s.

References

P. Cooper, C. S. Martin, and T. J. O'Hearn, 2016:  History of the Fluids Engineering Division.  Journal of Fluids Engineering, 138:  100801.

M. S. Petersen, 2002:  Summary of the Hydraulics Division- ASCE (1938-1988).  Environmental and Water Resources History, ed. by J. R. Rogers and A. J. Fredrich, Sessions at ASCE Civil Engineering Conference and Exposition, Nov. 3-7, 2002, Washington, DC., pp. 193-194.

Physics Today and fluid mechanics

Back in December, I discussed a couple recent items in Physics Today of great relevance to fluid mechanics.  I remarked that we don't get to see fluid mechanics on the cover of PT very often.  Well, lo and behold, the last couple months have proven me delightfully wrong!  The cover of the April issue featured "Fluid Dynamics of Dry Salt Lakes", referring to a "quick study" article by Beaume, Goehring, and Lasser.  It discusses a theory of groundwater convection as a possible explanation of polygonal patterns on dry lake beds ("salt polygons").  The very next month, the cover features "A Shocking Start to Stars", referring to a piece by Ceccarelli and Codella, on the role of shock waves in the interstallar medium, especially in star formation, and even in synthesizing certain precursor molecules of life itself.

However, I was personally most enthralled by another piece that also appeared in the May issue by meteorologist Tim Palmer, "The real butterfly effect and maggoty applies".  The title seems to have been taken from two earlier efforts, one coauthored by Palmer in 2019 on the real butterfly effect (published in Nonlinearity), and the other a 2002 PT Reference Frame article by Sir Michael Berry on singular limits.  Both these papers seem to have had a strong influence on this article, which mainly deals with weather predictibility, and more generally the predictability of nonlinear systems, including the limits of artificial intelligence-based prediction.  The article is very good, informative, and very readable.  I'd come across the singular limit concept in discussions of aerodynamic lift, where viscosity is the parameter that when taken to exactly zero (and not just a limit approaching zero) results in the impossibility of lift.  Reading Palmer's piece, and then Berry's, has helped me understand that there are other such examples in physics.

Theory, computation, and machine learning in climate science

This month's Physics Today features an excellent article by Schneider, Jeevanjee, and Socolow, "Accelerating Progress in Climate Science".  In particular, its philosophical orientation with regard to the interacting roles of theory, computation, and machine learning is one of the best articulated I have seen, and more broadly relevant than just for climate science.  The emerging role of machine learning in the study of the atmosphere is a topic I've written about previously (here and here).

The authors write, "Researchers have made deductive inferences from fundamental physical laws with some success.  But deducing, say, a coarse-grained description of clouds form the underlying fundamental physical laws has remained elusive.  Similarly, brute-force computing will not resolve all relevant spatial scales anytime soon.  Resolving just the meter-scale turbulence in low clouds globally would require about a factor of 10^11 increase in copmuter performance.  Such a performance boost is implausible in the coming decades and would still not suffice to handle droplet and ice-crystal formation."

They continue, "Machine learning (ML) has undeniable potential for harnessing the exponentially growing volume of Earth observations that is available.  But purely data-driven approaches cannot fully constrain the vast number of coupled degrees of freedom in climate models.  Moreover, the future changed climate we want to predict has no observed analogue, which creates challenges for ML methods because they do not easily generalize beyond the training data."

The authors go on to describe a concept they call parametric sparsity while comparing Newtonian gravity (with a single free parameter) to Ptolemian epicycles and equants, "the deep learning approach of its time."  They note that Newtonian gravity theory has a remarkable track record of "out-of-sample predictions, uncertainty estimates, and causal explanations."  Ptolemy's theory, like deep learning, is a massively parameterized model of empirical data, overfitted to the training data, but providing little guidance on what to expect outside the training data, the authors seem to argue.  The analogy is interesting but imperfect. As I noted previously, deep learning has demonstrated, under some circumstances, an ability to generalize beyond the training data.  I wrote then, "We do not know under what circumstances such generalization can reliably occur, and I believe any such claims about these generalizations must be validated with independent data sets."  Thus, the authors' skepticism about such generalizability is a welcome pragmatic attitude.

The authors write, "Climate science needs to predict a climate that hasn't been observed, on which no model can be trained, and that will only emerge slowly.  Generalizability beyond the observed sample is essential for climate predictions, and interpretability is necessary to have trust in models.  Additionally, uncertainties need to be quantified for proactive and cost-effective climate adaptation."  They advocate for the use of theory to develop coarse-grained models for use in computational simulations.  "Where theory reaches its limits, data-driven approaches can harness the detailed Earth observations now available." The authors' advocacy of theory-first, empirical modeling second, might be seen as an answer to Kerry Emanuel's concern about computing too much and thinking too little (discussed here).

I might depart a little from the authors in expressing some skepticism about the quality of uncertainty quantification.  Any such quantification is likely to be done in the context of the model itself, and thus fail to account for model uncertainty, which can never be fully quantified.  See also my discussion of "Escape from Model-Land" here.

Nonetheless, readers interested in climate science, and more broadly the interacting roles of theory, computation, and machine learning in the scientific endeavor (which truly must be coupled with experiment and observation) should check out the Physics Today article and think about how its ideas might apply to their own work.

Reference

T. Schneider, N. Jeevanjee, and R. Socolow, 2021:  Accelerating progress in climate science.  Physics Today, 74 (6), 44-51.

More on machine learning in weather and climate modeling

 My last post commented on the use of machine learning in Earth science modeling.  The Philosophical Transactions of the Royal Society, Series A, has just published a special issue on this topic, based on a workshop held at Oxford University in September 2019.  Here is a link to the introductory paper, and a link to one of the contributions, that deals with physically-aware machine learning models.  Ten case studies are discussed in the latter.

"In our quest for accurate simulations, are we computing too much and thinking too little?"

The title of this post is a quote from Kerry Emanuel's essay published earlier this year, "The Relevance of Theory for Contemporary Research in Atmospheres, Oceans, and Climate".  He expresses a concern that numerical models of these phenomena, and attempts to improve their predictive performance by brute force, have supplanted the value of theoretical reasoning in advancing atmospheric and oceanic science.  The need to cope with large data sets and computational methodology has supplanted the study of scientific theory and physics in the curriculum, resulting in an unbalanced focus by both students and researchers, in his view.  He presents the argument more forcefully and eloquently than I can, so DTLR recommends this essay to all its readers.

Reference

K. Emanuel, 2020:  The relevance of theory for contemporary research in atmospheres, oceans, and climate.  AGU Advances, 1 (2), e2019AV000129.

 

Escape From Model-Land

Earlier this week, Mark Buchanan's column in Nature Physics featured "The Limits of a Model" , concerning  the pitfalls of epidemiological forecasting, of course a topic very much in the news in the last few months.  Near the end Buchanan cites an intriguing paper by Thompson & Smith (2019), "Escape From Model-Land", a bracing discussion of why one should be very humble about using mathematical/empirical models.  It calls to mind another entertaining piece from over a decade ago, the "Financial Modeler's Manifesto" by Emanuel Derman and Paul Wilmott.

DTLR strongly recommends the Thompson & Smith piece, a candidate for required reading by every applied mathematician, applied statistician, and mathematical/computational modeler in every discipline.

Reference

Erica L. Thompson and Leonard A. Smith, 2019:  Escape From Model-Land.  Economics:  The Open-Access, Open-Assessment E-Journal, 13:  2019-40.

The AMS statement on weather analysis and forecasting

Following up on my last post on meteorology, I want to draw readers' attention to the American Meteorological Society's information statement on weather analysis and forecasting, which appeared in the Society's Bulletin recently, and can be found online here.  I found it to be very informative.

Dr. Marshall Shepherd's weather pet peeves

Check out Dr. Marshall Shepherd's blog post, "10 Common Myths and Misconceptions about the Science of Weather" at Forbes.com.  He is a distinguished professor at the University of Georgia and a former president of the American Meteorological Society.

This winter's weather does not reveal anything about global warming

Last month in Science, a group of five prominent atmospheric scientists from around the country published a letter cautioning us not to interpret this winter's severe weather through the lens of climate change (Wallace et al., 2014). This winter has witnessed the dip in the polar vortex over much of the U.S., resulting in all time low temperatures; there have also been a series of heavy snowstorms on the east coast. Speaking of these events, the authors write:

Some have been touting such stretches of extreme cold as evidence that global warming is a hoax, while others have been citing them as evidence that global warming is causing a “global weirding” of the weather. In our view, it is neither.

As climate scientists, we share the prevailing view in our community that human-induced global warming is happening and that, without mitigating measures, the Earth will continue to warm over the next century with serious consequences. But we consider it unlikely that those consequences will include more frigid winters.

Although such a hypothesis has been proposed, the authors do not find it corroborated with either “alternate observational analyses” nor climate model simulations. Moreover they “do not view the theoretical arguments underlying it as compelling.” The authors caution about mistaking coincidence for causation, and although they believe such hypotheses “deserve a fair hearing”, the authors seem to imply that this one is too half-baked to be made “the centerpiece of the public discourse on global warming.” They conclude:

Even in a warming climate, we could experience an extraordinary run of cold winters, but harsher winters in future decades are not among the most likely nor the most serious consequences of global warming.

Reference

John M. Wallace, Isaac M. Held, David W. J. Thompson, Kevin E. Trenberth, and John E. Walsh, 2014: Global warming and winter weather. Science, 343: 729-730.

Hurricane Sandy, climate change, and the limits of data science

Climate science is a very touchy subject, because unfortunately nearly all discussion of it becomes quickly entwined with politics. A reminder of this was recently discussed by Kerr (2013). In the President's State of the Union address a year ago, Mr. Obama said “We can choose to believe that Superstorm Sandy, and the most severe drought in decades, and the worst wildfires some states have ever seen were all just a freak coincidence. Or we can choose to believe in the overwhelming judgment of science—and act before it's too late.”

However, Kerr (2013) notes that “there is little or no evidence that global warming steered Sandy into New Jersey or made the storm any stronger. And scientists haven't even tried yet to link climate change with particular fires.” Kerr also points to a Republican Congressman's equally incorrect claim that “Extreme weather isn't linked to climate change.” Kerr states that several heat waves have indeed been “securely linked” to global warming. Kerr says that “Links between extreme weather and climate change are not only often scientifically suspect, they may also be a risky strategy to take climate change seriously.” After all, climate by definition is a statistical average of weather, which is what we experience on a day-to-day basis. The President, alas, was wrong.

Last March, I attended a lecture by Dr. Richard A. Anthes, an eminent meteorologist and president emeritus of the University Corporation for Atmospheric Research, and a former president of the American Meteorological Society. He pointed out that the track of Hurricane Sandy, with its left turn towards New Jersey, had been predicted days in advance by the ECMWF forecast model (European Center for Medium-Range Weather Forecasts). The U.S. forecast models were not able to give as early a warning, due to technical limitations of the computers and computer models (see my earlier post).

Let's use this example in a thought experiment on how data could be used to make a hurricane forecast. A purely empirical approach (whether by conventional statistical methods or by data mining/machine learning) would likely have failed: never before had a hurricane approached New Jersey from the east in late October. The ECMWF model uses data too, but not for statistical forecasting. It uses data as initial conditions to simulate the atmosphere using partial differential equations that incorporate subject matter knowledge of the physics and chemistry of the atmosphere and ocean. By doing so, it forecast the formation of the storm before it actually formed, as well as its subsequent track. The successful forecast of the ECMWF model was interpreted correctly by political authorities and heeded by the public, saving tens of thousands of lives. If you want an example of science at its best, here it is.

Make no mistake: meteorology as a science does make very judicious use of statistical and monte carlo methods. The atmospheric sciences, however, are driven primarily by methods based on subject matter knowledge, not strictly empirical methods such as those used by statisticians and data scientists (“superficial statistics” in the devastating words of Salby, 2012). Note that both approaches are equally data hungry.

Let's return to climate now. The whole point of climate change is that data in the future will not be like data from the past. As Showstack (2013) reports, Kathryn Sullivan, acting administrator of the National Oceanic and Atmospheric Administration (NOAA) gave the keynote address at the National Research Council's Board on Earth Sciences and Resources in November. She said, “The past is no longer prologue when it comes to the risks we bear at any given place on this planet. The statistical pattern of our past cannot be relied upon fully to tell us what our future will be.” Isn't this a conundrum for a statistician or a data scientist?  What good is the training data when we know it will not be informative about data from the future? 

In my view, the solution to all this is to use first principles modeling, as climate scientists do. As with meteorology, in climatology knowledge of the physics and chemistry of the atmosphere, embodied in the partial differential equations of climate modeling, is preferred to the methods of statistics and data science for predicting both the weather and the climate.

References

 

 

Richard A. Kerr, 2013: In the hot seat. Science, 342: 688-689.

Murray L. Salby, 2012: Physics of the Atmosphere and Climate. Cambridge University Press, p. xvi.

Randy Showstack, 2013: Earth sciences and societal needs explored at National Research Council meeting. EOS, Transactions of the American Geophysical Union, 94 (48): 457-459.

The AMS Data Policy Statement: Full and Open Access to Data

The American Meteorological Society currently has a draft statement open to comments by members until Oct. 3, on Full and Open Access to Data.  As a member I submitted the following comment:

"I applaud the principles outlined in this document, and AMS's willingness to take a definite stand on the issues discussed. I am particularly in favor of strong encouragement for academic journals and funding agencies to *require* that data sources be clearly identified and publicly available, unless a justification can be given.  What the policy fails to address, perhaps because it is out of scope, is a more complete endorsement of the principles of reproducible research, which would also require making computer code publicly available, and also specification of software options and settings used, and finally full specification of any data reduction or manipulation procedures carried out between the raw and the analyzed data sets (e.g., filtering, interpolation to convert non-equally spaced time series into equally spaced time seris, etc.)  Thank you for this opportunity to comment."

The Diffusion Tensor Literary Review (DTLR) similarly endorses the draft statement on Full and Open Access to Data. 

Welcome to “Tide” and “Gyre”, supercomputers for operational weather forecasting

Last thursday, the National Weather Service (NWS) brought online its two new “clone” IBM supercomputers, Tide and Gyre, for operational weather forecasting. The machines are the workhorses of the NWS National Centers for Environmental Prediction (NCEP)'s Weather and Climate Operational Supercomputing System (WCOSS). The two identical supercomputers are located in different places (primary machine at Reston, VA; backup at Orlando, FL).

Patrick Thibodeau's write-up at Computerworld seems to have led media coverage of the event. He refers to last year's Hurricane Sandy, which engendered “a belief that the European Center for Medium-Range Weather Forecasts (ECMWF) had a better storm track model further out. Criticism over the U.S. forecasting ability has followed post Sandy.” Bringing the new computers online, replacing the previously used pair of IBM supercomputers, is a step in the right direction. (A planned phase II of the transition will lead to further improvements.) However, the U.S. will also need to maintain investments in Earth-observing weather satellites, as noted (for instance) by Stephen Stirling of the New Jersey Star Ledger.

The NWS computers do work of national importance, and it is reassuring to read about the verification and validation of the systems prior to going live. Further details can be found in this post by Steve Tracton at the Washington Post. He describes sensitive dependence on initial conditions, manifested as a divergence in forecasts from the same forecast model run on both the old and new supercomputers. This phenomenon, as he points out, is closely related to the path-breaking work on chaos in meteorology and computational science by the late Edward N. Lorenz (1963, 1989).

Update (29 Aug 2013):  see also the NOAA press release.

 References

 

E.N. Lorenz, 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20: 130-141.

E. N. Lorenz, 1989: Computational chaos -- a prelude to computational instability.  Physica D, 35: 299-317.