Some news on solar physics

This week's Physics magazine from the American Physical Society features an interesting article by Marric Stephens about an apparent anomaly - a contradiction between theoretical expectations and observational data regarding the sun's sodium D1 absorption line.  Stephens reports on a new paper by a Swiss/Spanish/German team that resolves the paradox by replacing a seemingly reasonable assumption:  that "the anisotropic radiation field that pumps the atoms of the solar atmosphere was assumed constant with wavelength over the very small spectral interval spanned by the nearby hyperfine structure components of both the sodium $D_1$ and $D_2$ lines."  The authors Ballester, Belluzi, and Bueno apply a theory of polarized radiative transfer that takes "into account the detailed spectral structure of the radiation, together with the effects of magnetic fields of arbitrary strength and elastic collisions in a realistic atomic model including [hyperfine structure]" .  There is a link to the paper itself, which is open source.  A nice tale of resolving an apparent conflict between theory and observation.

Reference

Ballester, Belluzi, and Bueno, 2021, Phys. Rev. Lett. 127:  081101.

Steven Weinberg 1933-2021

The world has lost one of its great theoretical physicists, Dr. Steven Weinberg, 1979 Nobel laureate.  Weinberg was also a great writer, authoring a series of influential textbooks, as well as works for the general public.  DTLR has always been admirer of his general writing.

The New York Review of Books, to which he was a longtime contributor, has made this 2001 essay by him available for reading in his honor.  I strongly recommend it.

The proton radius puzzle and the hazards of combining data from multiple studies

If journalism is a first draft of history, Physics World writer Edwin Cartlidge has done a superb job this month of reporting on the "Proton Radius Puzzle" and the pitfalls of combining data from multiple studies.  Cartlidge's piece is also a superb case study of a phenomenon described by David Bailey a few years ago.

Over time, a number of experiments around the world, using different physical principles, attempted to measure the radius of the proton.  An international group, CODATA, has the task of compiling all such data and basically reporting the community's best estimate.  This is done by first voting on which studies should be included; then the data are simply (weighted) averaged; presumably the weights are determined by the error bars reported by the individual studies.  The thus combined estimate, it is hoped, will have a more accurate point estimate, and narrow error bars, than any of the individual findings.  The former chair of the CODATA group working on the proton radius is quoted in Cartlidge's article arguing that this process incorporates all "individually credible results" but passes no judgment on whether each of those results is "right or wrong", a task that "would require superhuman powers".

Well, for about eight years, the measurement by the CREMA experiment, using a unique muon-based principle, was excluded from the average, as it was an outlier compared to other reports (in exactly the sense that Bailey has described).  However, in the interim, other groups using the more conventional approaches started to obtain results comparable to the lower value given by CREMA.  In 2018 CODATA finally incorporated the outlying results, though the stated error bars for the combined estimate had increased, unsurprisingly.  See Cartlidge's article for the twists and turns of the story.

DTLR's interest here is in the whole concept of combining data.  Something like this is widely practiced in statistics, under the name "meta-analysis".  I consider this poor practice, because it sweeps under the rug potential systematic errors in the individual results.  In the proton radius case, Cartlidge even seems to suggest that groupthink might have been at play in the CODATA decisions.

Here is DTLR's opinion about combinging data from multiple studies.  Don't do it.  Instead of meta-analysis, the individual study results, with their error bars, should simply be displayed together.  Users should be directed to critically review the study design, execution, analysis, and reporting of the individual studies, seeking out differences among them.  Authors of systematic reviews should use their judgment and discuss the similarities and differences, without blindly pooling all the data together.  Cartlidge writes, "the CREMA result was not really at odds with individual spectroscopy experiments – all but one differed by no more than 1.5 standard deviations, or σ. The only significant disparity – of at least 5 σ – arose when the conventional data were averaged and the error bars shrunk. But that disparity could only be maintained if the muon result itself was kept out of the fitting process – given how much it would otherwise shift the CODATA average towards itself."  My interpretation is that the artificial task of combining the data drove the source of confusion; this would have been avoided by simply presenting all the individual study results separately.  The field has clearly not reached sufficient maturity for a combined "best" estimate to be meaningful, in my opinion, and this is probably even more true of the meta-analyses often reported in the medical and public health literature.

Just days after Cartlidge's article came out, another one authored by him was published that also has combining data at its heart.  This one was about gravitational waves, but the story is complicated even further by the waveform modeling required to interpret gravitaitional wave signals.

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.

The 200th Anniversary of the Navier-Stokes Equations

Earlier today, Nature Physics published a "Measure for Measure" note by Oxford professor Julia Yeomans about the Navier-Stokes Equations and the Reynolds Number.  She reminds us that next year, 2022, will be the 200th anniversary of the first appearance of these equations at the hand of Claude-Louis Navier in 1822.  Investigating further (Rouse & Ince, 1957; Darrigol, 2005; Eckert, 2006), it appears that Navier read his papers at the French Royal Academy of Sciences that year, but the written publication appeared later in 1827.  Navier's original formulation was based on a now-discredited molecular model.  Meanwhile also in 1822, A.L. Cauchy published his theory of stress in continua.  S. D. Poisson, Barre de Saint-Venant, and I. S. Gromeka are others who contributed to the theoretical development of the Navier-Stokes equations.  In most historians' view, the definitive derivation of the Navier-Stokes equations was given by George Gabriel Stokes in 1845.  Nonetheless it was indeed Navier in 1822 who first presented the equations.  Prof. Yeomans also discusses Osborne Reynolds' 1883 paper on the nondimensional parameter named in his honor.  

DTLR is grateful to Nature Physics and Prof. Yeomans for bringing these notions to the attention of the journal's readers.  As I've written before, fluid mechanics has been underrepresented in the U.S. physics curriculum, and it's nice to see pieces like this in physics journals.

References

O. Darrigol, 2005:  Worlds of Flow:  A History of Hydrodynamics from the Bernoullis to Prandtl.  Oxford University Press.

M. Eckert, 2006:  The Dawn of Fluid Dynamics:  A Discipline between Science and Technology.  Wiley-VCH.

H. Rouse and S. Ince, 1957:  History of Hydraulics.  Iowa Institute for Hydraulic Research.

J. M. Yeomans, 2021:  Fluid flows on many scales.  Nature Physics, 17:  756.

Ham radio and space weather

A superb example of "crowdsourced science" is described in this piece in this month's Eos. Check it out!

Reference

Collins, K., D. Kazdan, and N. A. Frissell (2021), Ham radio forms a planet-sized space weather sensor network, Eos, 102, https://doi.org/10.1029/2021EO154389. Published on 09 February 2021.

A tribute to the publishers of classic engineering science books: the Oxford Engineering Science Series

Back in December DTLR had a series of posts in tribute to the publishers of classic books in physics and atmospheric science.  I'd like to return to that theme by paying homage today to the Oxford Engineering Science Series.  As far as I know, the series began with the publication in 1974 of Synthesis of Planar Antenna Sources, by Donald R. Rhodes (Volume 1 of the series).  The last contributions I know of are Volume 51, Electromagnetic Waveguides and Transmission Lines, by Frank Olyslager, published in 1999, and a second edition of Volume 14, Engineering Rheology, by Roger I. Tanner, published in 2000.  

Here are a few members of the series in my personal book collection.

Volumes 2, 10, and 36 of the Oxford Engineering Science Series

As can be seen from the photo, these books were also branded as Oxford Science Publications.  Also illustrated in the photo, Peter Hagedorn's Non-Linear Oscillations (Volume 10) was another example of a member of the series that came out in a second edition (1988).  Unfortunately now even this edition appears to be out of print.  Other volumes with multiple editions include Collier & Thome's Convective Boiling and Condensation (3d edition, 1996) and Wesson's Tokamaks (4th edition, 2011), though this latter moved to the same publisher's International Series of Monographs in Physics (a series that continues to go strong today).  Other volumes actually appear in two-volume sets (collectively issued the same series number):  these include Jones' Methods in Electromagnetic Wave Propagation (1979) and Thornton's Science and Practice of Liquid-Liquid Extraction (1992).  Jones' book was issued in a second edition co-published by IEEE and Wiley (1994), in their Series on Electromagnetic Wave Theory.

In the photo, Hagedorn's book is flanked by Les Woods' 1975 book, The Thermodynamics of Fluid Systems (Vol. 2), and Beris & Edwards' 1994 contribution, Thermodynamics of Flowing Systems with Internal Microstructure (vol. 36).  (Woods was one of the series editors for many years as well.)  The Hagedorn and Beris & Edwards volumes illustrate the elegant blue and red livery of the paperback and hardback volumes of the series in its middle years.  The hardbacks even had gold trim on the covers; the photo does not do it justice.  Unfortunately, the paperback livery in the most recent years resembles that of other (less elegant) Oxford Science Publications.

Unfortunately I do not own a copy of Volume 25, The Physics of Fluid Turbulence, by W. David McComb (1990).  Interestingly, McComb later (2014) published a second work on turbulence, Homogeneous, Isotropic Turbulence:  Phenomonology, Renormalization and Statistical Closures, which appeared instead in Oxford's International Series of Monographs in Physics, where Wesson's Tokamaks also moved, as mentioned earlier.

As for the Oxford Engineering Science Series, like Academic Press's International Geophysics Series that I wrote about earlier, the publisher websites list them as active, but no new volumes have been published in a long time.  I do wonder if any of these series will continue (the McGraw-Hill and Addison-Wesley series I wrote about earlier seem to be long-defunct), given the dynamics of scientific publishing in recent years.