#turbulence — Public Fediverse posts

💁🏻‍♀️ TIL: 🦅💨 The nankeen #kestrel, a tiny Australian #falcon, flies with exceptional stability in turbulent winds, inspiring one international #research team.

Wind tunnel #experiments showed wing and tail extensions helped #birds maintain lift and reduce #flight irregularities. The team aims to integrate these features into small #drones as #climatechange increases atmospheric #turbulence.

👉 https://www.popsci.com/technology/falcon-aircraft-turbulence/

#biomimicry #nankeekestrel #engineering #robotics #climate #aviation #australia #animals #technology #tech

Weekly Update from the Open Journal of Astrophysics – 07/02/2026

It’s Saturday once more so time for another update of activity at the Open Journal of Astrophysics. Since the last update we have published a further six papers, bringing the number in Volume 9 (2026) to 24 and the total so far published by OJAp up to 472.

I will continue to include the posts made on our Mastodon account (on Fediscience) to encourage you to visit it. Mastodon is a really excellent service, and a more than adequate replacement for X/Twitter which nobody should be using; these announcement also show the DOI for each paper.

The first paper to report this week is “The Impact of Star Formation and Feedback Recipes on the Stellar Mass and Interstellar Medium of High-Redshift Galaxies” by Harley Katz (U. Chicago, USA), Martin P. Rey (U. Oxford, UK), Corentin Cadiou (Lund U., Sweden) Taysun Kimm (Yonsei U., Korea) and Oscar Agertz (Lund). This paper was published on Monday 2nd February 2026 in the folder Astrophysics of Galaxies. It introduces MEGATRON, a new model for galaxy formation simulations, highlighting that feedback energy controls star formation at high redshift and highlighting the importance of the interstellar medium.

The overlay is here:

You can find the officially accepted version on arXiv here and the announcement on Fediverse here:

https://fediscience.org/@OJ_Astro/116000695648050758

The second paper is “Photometric Redshifts in JWST Deep Fields: A Pixel-Based Alternative with DeepDISC” by Grant Merz (U. Illinois at Urbana-Champaign) and 6 others, all based in the USA. This paper was published on Monday February 2nd 2026 in the folder Instrumentation and Methods for Astrophysics. This paper explores the effectiveness of the DeepDISC machine learning algorithm in estimating photometric redshifts from near-infrared data, demonstrating its potential for larger image volumes and spectroscopic samples

The overlay for this one is here:

The official version of the paper can be found on arXiv here and the Fediverse announcement here:

https://fediscience.org/@OJ_Astro/116000777572439111

Next, published on Wednesday 4th February in the folder Astrophysics of Galaxies, is “Inferring Interstellar Medium Density, Temperature, and Metallicity from Turbulent H II Regions” by Larrance Xing (U. Chicago, USA), Nicholas Choustikov (U. Oxford, UK), Harley Katz (U. Chicago) and Alex J. Cameron (DAWN, Denmark). This paper argues that supersonic turbulenc affects the interpretation of H II region properties, potentially impacting inferred metallicity, ionization, and excitation from in nebular emission lines, motivating more extensive modelling.

The overlay is here:

The official version can be found on arXiv here and the Fediverse announcement is here:

https://fediscience.org/@OJ_Astro/116011384659092223

The fourth paper this week, also published on Wednesday 4th February, but in the folder Solar and Stellar Astrophysics, is “A Systematic Search for Big Dippers in ASAS-SN” by B. JoHantgen, D. M. Rowan, R. Forés-Toribio, C. S. Kochanek, & K. Z. Stanek (Ohio State University, USA), B. J. Shappee (U. Hawaii, USA), Subo Dong (Peking University), J. L. Prieto Universidad Diego Portales, Chile) and Todd A. Thompson (Ohio State). This study identifies 4 new dipper stars and 15 long-period eclipsing binary candidates using ASAS-SN light curves and multi-wavelength data, categorizing them based on their characteristics.

Here is the overlay:

The official version can be found on arXiv here and the Fediverse announcement is here:

https://fediscience.org/@OJ_Astro/116011460612040834

Fifth, and next to last this week we have “Unveiling the drivers of the Baryon Cycles with Interpretable Multi-step Machine Learning and Simulations” by Mst Shamima Khanom, Benjamin W. Keller and Javier Ignacio Saavedra Moreno (U. Memphis, USA). This paper was published on Thursday 5th February 2026 in the folder Astrophysics of Galaxies. This study uses machine learning methods to understand how galaxies lose or retain baryons, highlighting the relationship between baryon fraction and various galactic measurements.

The overlay is here:

The accepted version can be found on arXiv here, and the fediverse announcement is here:

https://fediscience.org/@OJ_Astro/116016883984380622

Finally for this week we have “The Bispectrum of Intrinsic Alignments: II. Precision Comparison Against Dark Matter Simulations” by Thomas Bakx (Utrecht U., Netherlands), Toshiki Kurita (MPA Garching, Germany), Alexander Eggemeier (U. Bonn, Germany), Nora Elisa Chisari (Utrecht) and Zvonimir Vlah (Ruđer Bošković Institute, Croatia). This paper was accepted in December, but publication got delayed by the Christmas effect so was published on February 6th 2026, in the folder Cosmology and Nongalactic Astrophysics. This study uses N-body simulations to accurately measure three-dimensional bispectra of halo intrinsic alignments and dark matter overdensities, providing a method to determine higher order shape bias parameters.

The overlay is here:

You can find the published version of the article here, and the Mastodon announcement is here:

https://fediscience.org/@OJ_Astro/116022562915557971

And that concludes this week’s update. I will do another next Saturday.

The butterfly effect — that the flapping of a butterfly’s wings in Brazil can cause a tornado in Texas — expresses the sensitivity of a chaotic system to initial conditions. In essence, because we can’t possibly track every butterfly in Brazil, we’ll never perfectly predict tornadoes in Texas, even if the equations behind our weather forecast are deterministic.

But this interpretation doesn’t fully capture the subtleties of the situation. With fluid dynamics, the small scales of a flow — like the turbulence in an individual cloud — are linked to the largest scales in the flow — for example, a hurricane. For short times, we’re actually quite good at predicting those large scales; our weather forecasts can distinguish sunny days and cloudy ones a week out. But at smaller scales, the forecast errors pile up quickly. No one can forecast that an individual cloud will form over your house three days from now. And because the small scales are linked to the larger scales, the uncertainties from the small scale cascade upward, limiting how far into the future we can reliably predict the weather.

And, unfortunately, drilling down to capture smaller and smaller scales in our models can’t fix the problem, unless our initial uncertainties are identically zero. To get around this problem, weather forecasters instead use ensemble forecasting, where they run many simulations of the weather with slightly different initial conditions. Those differences in initial conditions let the forecasters play with those initial uncertainties — how accurate is the temperature reading from that station? How reliable is the instrument reporting that humidity? How old is the satellite data coming in? Once all the forecasts are run, they can see how many predicted sunny days versus rainy ones, which ones resulted in severe weather, and so on. Often the probabilities we see in our weather app — like 30% chance of rain — depend on factors including how many of the forecasts resulted in rain.

Unfortunately, this butterfly effect permanently limits just how far into the future we can predict weather — at least until we fully understand the nature of the Navier-Stokes equations. For much more on this interesting aspect of chaos, check out this Physics Today article. (Image credit: NASA; see also T. Palmer at Physics Today)

https://fyfluiddynamics.com/2024/07/the-real-butterfly-effect/

#atmosphericScience #butterflyEffect #chaos #chaosTheory #clouds #fluidDynamics #mathematics #meteorology #NavierStokesEquations #physics #science #turbulence #weather

The butterfly effect — that the flapping of a butterfly’s wings in Brazil can cause a tornado in Texas — expresses the sensitivity of a chaotic system to initial conditions. In essence, because we can’t possibly track every butterfly in Brazil, we’ll never perfectly predict tornadoes in Texas, even if the equations behind our weather forecast are deterministic.

But this interpretation doesn’t fully capture the subtleties of the situation. With fluid dynamics, the small scales of a flow — like the turbulence in an individual cloud — are linked to the largest scales in the flow — for example, a hurricane. For short times, we’re actually quite good at predicting those large scales; our weather forecasts can distinguish sunny days and cloudy ones a week out. But at smaller scales, the forecast errors pile up quickly. No one can forecast that an individual cloud will form over your house three days from now. And because the small scales are linked to the larger scales, the uncertainties from the small scale cascade upward, limiting how far into the future we can reliably predict the weather.

And, unfortunately, drilling down to capture smaller and smaller scales in our models can’t fix the problem, unless our initial uncertainties are identically zero. To get around this problem, weather forecasters instead use ensemble forecasting, where they run many simulations of the weather with slightly different initial conditions. Those differences in initial conditions let the forecasters play with those initial uncertainties — how accurate is the temperature reading from that station? How reliable is the instrument reporting that humidity? How old is the satellite data coming in? Once all the forecasts are run, they can see how many predicted sunny days versus rainy ones, which ones resulted in severe weather, and so on. Often the probabilities we see in our weather app — like 30% chance of rain — depend on factors including how many of the forecasts resulted in rain.

Unfortunately, this butterfly effect permanently limits just how far into the future we can predict weather — at least until we fully understand the nature of the Navier-Stokes equations. For much more on this interesting aspect of chaos, check out this Physics Today article. (Image credit: NASA; see also T. Palmer at Physics Today)

https://fyfluiddynamics.com/2024/07/the-real-butterfly-effect/

#atmosphericScience #butterflyEffect #chaos #chaosTheory #clouds #fluidDynamics #mathematics #meteorology #NavierStokesEquations #physics #science #turbulence #weather