Tyler O'Neal, Staff Editor STORAGE

Telescope, spacecraft join forces to probe deep into Jupiter's atmosphere

NASA's Hubble Space Telescope and the ground-based Gemini Observatory in Hawaii have teamed up with the Juno spacecraft to probe the mightiest storms in the solar system, taking place more than 500 million miles away on the giant planet Jupiter.

A team of researchers led by Michael Wong at the University of California, Berkeley, and including Amy Simon of NASA's Goddard Space Flight Center in Greenbelt, Maryland, and Imke de Pater also of UC Berkeley, are combining multiwavelength observations from Hubble and Gemini with close-up views from Juno's orbit about the monster planet, gaining new insights into turbulent weather on this distant world.

"We want to know how Jupiter's atmosphere works," said Wong. This is where the teamwork of Juno, Hubble and Gemini comes into play.

Radio 'Light Show'

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This graphic shows observations and interpretations of cloud structures and atmospheric circulation on Jupiter from the Juno spacecraft, the Hubble Space Telescope, and the Gemini Observatory. By combining the Juno, Hubble and Gemini data, researchers are able to see that lightning flashes are clustered in turbulent regions where there are deep water clouds and where moist air is rising to form tall convective towers similar to cumulonimbus clouds (thunderheads) on Earth. The bottom illustration of lightning, convective towers, deep water clouds and clearings in Jupiter's atmosphere is based on data from Juno, Hubble and Gemini, and corresponds to the transect (angled white line) indicated on the Hubble and Gemini map details. The combination of observations can be used to map the cloud structure in three dimensions and infer details of atmospheric circulation. Thick, towering clouds form where moist air is rising (upwelling and active convection). Clearings form where drier air sinks (downwelling). The clouds are shown rise five times higher than similar convective towers in the relatively shallow atmosphere of Earth. The region illustrated covers a horizontal span of one-third greater than that of the continental United States.

Credits: NASA, ESA, M.H. Wong (UC Berkeley), A. James, and M.W. Carruthers (STScI), and S. Brown (JPL)

Jupiter's constant storms are gigantic compared to those on Earth, with thunderheads reaching 40 miles from base to top — five times taller than typical thunderheads on Earth — and powerful lightning flashes up to three times more energetic than Earth's largest "superbolts."

Like lightning on Earth, Jupiter's lightning bolts act like radio transmitters, sending out radio waves as well as visible light when they flash across the sky.

Every 53 days, Juno races low over the storm systems detecting radio signals known as "sferics" and "whistlers," which can then be used to map lightning even on the dayside of the planet or from deep clouds where flashes are not otherwise visible.

Coinciding with each pass, Hubble and Gemini watch from afar, capturing high-resolution global views of the planet that are key to interpreting Juno's close-up observations. "Juno's microwave radiometer probes deep into the planet's atmosphere by detecting high-frequency radio waves that can penetrate through the thick cloud layers. The data from Hubble and Gemini can tell us how thick the clouds are and how deep we are seeing into the clouds," Simon explained.

By mapping lightning flashes detected by Juno onto optical images captured of the planet by Hubble and thermal infrared images captured at the same time by Gemini, the research team has been able to show that lightning outbreaks are associated with a three-way combination of cloud structures: deep clouds made of water, large convective towers caused by an upwelling of moist air — essentially Jovian thunderheads — and clear regions presumably caused by downwelling of drier air outside the convective towers.

The Hubble data show the height of the thick clouds in the convective towers, as well as the depth of deep water clouds. The Gemini data clearly reveal the clearings in the high-level clouds where it is possible to get a glimpse down to the deep water clouds.

Wong thinks that lightning is common in a type of turbulent area known as folded filamentary regions, which suggests that moist convection is occurring in them. "These cyclonic vortices could be internal energy smokestacks, helping release internal energy through convection," he said. "It doesn't happen everywhere, but something about these cyclones seems to facilitate convection."

The ability to correlate lightning with deep water clouds also gives researchers another tool for estimating the amount of water in Jupiter's atmosphere, which is important for understanding how Jupiter and the other gas and ice giants formed, and therefore how the solar system as a whole formed.

While much has been gleaned about Jupiter from previous space missions, many of the details — including how much water is in the deep atmosphere, exactly how heat flows from the interior and what causes certain colors and patterns in the clouds — remain a mystery. The combined result provides insight into the dynamics and three-dimensional structure of the atmosphere.

Seeing a 'Jack-O-Lantern' Red Spot

With Hubble and Gemini observing Jupiter more frequently during the Juno mission, scientists are also able to study short-term changes and short-lived features like those in the Great Red Spot.

Images from Juno as well as previous missions to Jupiter revealed dark features within the Great Red Spot that appear, disappear and change shape over time. It was not clear from individual images whether these are caused by some mysterious dark-colored material within the high cloud layer, or if they are instead holes in the high clouds — windows into a deeper, darker layer below.

Now, with the ability to compare visible-light images from Hubble with thermal infrared images from Gemini captured within hours of each other, it is possible to answer the question. Regions that are dark in visible light are very bright in infrared, indicating that they are, in fact, holes in the cloud layer. In cloud-free regions, heat from Jupiter's interior that is emitted in the form of infrared light — otherwise blocked by high-level clouds — is free to escape into space and therefore appears bright in Gemini images.

"It's kind of like a jack-o-lantern," said Wong. "You see bright infrared light coming from cloud-free areas, but where there are clouds, it's really dark in the infrared." {module INSIDE STORY}

various images of Jupiter's Great Red Spot

Credits: NASA, ESA, and M.H. Wong (UC Berkeley) and team

The above images of Jupiter's Great Red Spot were made using data collected by the Hubble Space Telescope and the Gemini Observatory on April 1, 2018. By combining observations captured at almost the same time from the two different observatories, astronomers were able to determine that dark features on the Great Red Spot are holes in the clouds rather than masses of dark material.

Upper left (wide view) and lower left (detail): The Hubble image of sunlight (visible wavelengths) reflecting off clouds in Jupiter’s atmosphere shows dark features within the Great Red Spot.

Upper right: A thermal infrared image of the same area from Gemini shows heat emitted as infrared energy. Cool overlying clouds appear as dark regions, but clearings in the clouds allow bright infrared emission to escape from warmer layers below.

Lower middle: An ultraviolet image from Hubble shows sunlight scattered back from the hazes over the Great Red Spot. The Great Red Spot appears red in visible light because these hazes absorb blue wavelengths. The Hubble data show that the hazes continue to absorb even at shorter ultraviolet wavelengths.

Lower right: A multiwavelength composite of Hubble and Gemini data shows visible light in blue and thermal infrared in red. The combined observations show that areas that are bright in infrared are clearings or places where there is less cloud cover blocking heat from the interior.

The Hubble and Gemini observations were made to provide a wide-view context for Juno’s 12th pass (Perijove 12).

Hubble and Gemini as Jovian Weather Trackers

The regular imaging of Jupiter by Hubble and Gemini in support of the Juno mission is proving valuable in studies of many other weather phenomena as well, including changes in wind patterns, characteristics of atmospheric waves, and the circulation of various gases in the atmosphere.

Hubble and Gemini can monitor the planet as a whole, providing real-time base maps in multiple wavelengths for reference for Juno's measurements in the same way that Earth-observing weather satellites provide context for NOAA's high-flying Hurricane Hunters.

"Because we now routinely have these high-resolution views from a couple of different observatories and wavelengths, we are learning so much more about Jupiter's weather," explained Simon. "This is our equivalent of a weather satellite. We can finally start looking at weather cycles."

Because the Hubble and Gemini observations are so important for interpreting Juno data, Wong and his colleagues Simon and de Pater are making all of the processed data easily accessible to other researchers through the Mikulski Archives for Space Telescopes (MAST) at the Space Telescope Science Institute in Baltimore, Maryland.

"What's important is that we've managed to collect this huge data set that supports the Juno mission. There are so many applications of the data set that we may not even anticipate. So, we're going to enable other people to do science without that barrier of having to figure out on their own how to process the data," Wong said.

Hernquist, Springer win half-million dollars Gruber Cosmology Prize

Tyler O'Neal, Staff Editor STORAGE

The 2020 Gruber Cosmology Prize recognizes Lars Hernquist, Center for Astrophysics | Harvard & Smithsonian, and Volker Springel, Max Planck Institute for Astrophysics, for their defining contributions to cosmological simulations, a method that tests existing theories of, and inspires new investigations into, the formation of structures at every scale from stars to galaxies to the universe itself.

Hernquist and Springel will divide the $500,000 award, and each will receive a gold laureate pin at a ceremony that will take place later this year. The award recognizes their transformative work on structure formation in the universe, and development of numerical algorithms and community codes further used by many other researchers to significantly advance the field.

Hernquist was a pioneer in cosmological simulations when he joined the fledgling field in the late 1980s, and since then he has become one of its most influential figures. Springel, who entered the field in 1998 and first partnered with Hernquist in the early 2000s, has written and applied several of the most widely used codes in cosmological research. Together Hernquist and Springel constitute, in the words of one Gruber Prize nominator, "one of the most productive collaborations ever in cosmology." {module INSIDE STORY}

Computational simulations in cosmology begin with the traditional source of astronomical data: observations of the universe. Then, through a combination of theory and known physics that might approximate initial conditions, the simulations recreate the subsequent processes that would have led to the current structure. By comparing the properties of the simulated universe and galaxies to observations the validity of the underlying cosmological model can be tested.

This tool has allowed Hernquist and Springel, either individually or collaboratively, to show that information from the cosmic microwave background (the relic radiation from the Big Bang) and light spectra from quasars are reliable predictors of present-day galactic structures. They have also used computational simulations to test theories relating to cold dark matter (the invisible matter that comprises roughly four-fifths of the universe's matter) and dark energy (a mysterious force causing an accelerated late-time expansion of the universe), and how they in concert with ordinary baryons give rise to today's visible structures.

In addition to their own discoveries, Hernquist and Springel have provided the means for other researchers to transform cosmology. For instance, Hernquist, Springel, and their collaborators have emphasized the need for supercomputer simulations to incorporate feedback--the portion of the outflow of material (such as gas) that feeds back into evolutionary processes. In 2005, working with a collaborator (Tiziana Di Matteo), they demonstrated that black-hole feedback determines the growth relationship between supermassive black holes and their host galaxies.

Thanks to their example, feedback is now a standard component of cosmological simulations at virtually every scale, from stellar evolution, protoplanetary disks, supermassive black holes, gas physics in galaxies, and galaxy mergers, to dark matter physics that determines the distribution of superclusters of galaxies into web-like tendrils.

Hernquist and Springel have also written several codes that cosmologists consider indispensable. Hernquist (along with Neal Katz) created TreeSPH, which Hernquist and, subsequently, other researchers used to investigate large-scale structures. Springel wrote two codes that today dominate cosmological research. In 2001 he (with Naoki Yoshida and Simon White) introduced GADGET, which he used in creating the Millennium Simulation, the first dark-matter-only simulation to encompass a representative volume of the universe. The resulting series of images provided a vivid and compelling set of images that helped popularize the idea of the "cosmic web." Springel also led the creation of AREPO, a moving mesh simulation code which he and Hernquist (and a team of collaborators) subsequently used in the creation of Illustris, a 2014 simulation of the formation of the galaxy distribution across a broad area of the universe.

The problems of cosmic structure formation and the formation and evolution of galaxies are extremely complex, so much so that numerical simulations are the only practical way at present to construct a full theoretical model. The remarkable success of contemporary models such as Illustris, which can reproduce properties of the universe from its largest structures to individual galaxies, over nearly the full history of cosmic time, is the result of a triumphal marriage between state-of-the-art computation and deep astrophysical insights. This year's Gruber Cosmology Prize recognizes the leading role in this breakthrough played by Lars Hernquist and Volker Springel.

All disease models are 'wrong,' but CU computer scientists are working to fix that

Tyler O'Neal, Staff Editor STORAGE

An international team of researchers has developed a new mathematical tool that could help scientists to deliver more accurate predictions of how diseases, including COVID-19, spread through towns and cities around the world.

Rebecca Morrison, an assistant professor of computer science at the University of Colorado Boulder, led the research. For years, she has run a repair shop of sorts for mathematical models--those strings of equations and assumptions that scientists use to better understand the world around them, from the trajectory of climate change to how chemicals burn up in an explosion.

As Morrison put it, "My work starts when models start to fail."

She and her colleagues recently set their sights on a new challenge: epidemiological models. What can researchers do, in other words, when their forecasts for the spread of infectious diseases don't match reality? {module INSIDE STORY}

In a study published in the journal Chaos, Morrison and Brazilian mathematician Americo Cunha turned to the 2016 outbreak of the Zika virus as a test case. They report that a new kind of tool called an "embedded discrepancy operator" might be able to help scientists fix models that fall short of their goals--effectively aligning model results with real-world data.

Morrison is quick to point out that her group's findings are specific to Zika. But the team is already trying to adapt their methods to help researchers to get ahead of a second virus, COVID-19.

"I don't think this tool is going to solve any epidemiologic crisis on its own," Morrison said. "But I hope it will be another tool in the arsenal of epidemiologists and modelers moving forward."

When models fail

The study highlights a common issue that modelers face.

"There are very few situations where a model perfectly corresponds with reality. By definition, models are simplified from reality," Morrison said. "In some way or another, all models are wrong."

Cunha, an assistant professor at Rio de Janeiro State University, and his colleagues ran up against that very problem several years ago. They were trying to adopt a common type of disease model--called a Susceptible, Exposed, Infected, or Recovered (SEIR) model--to recreate the Zika virus outbreak from start to finish. In 2015 and 2016, this pathogen ran rampant through Brazil and other parts of the world, causing thousands of cases of severe birth defects in infants.

The problem: No matter what the researchers tried, their results didn't match the recorded number of Zika cases, in some cases miscalculating the number of infected people by tens of thousands.

Such a shortfall isn't uncommon, Cunha said.

"The actions you take today will affect the course of the disease," he said. "But you won't see the results of that action for a week or even a month. This feedback effect is extremely difficult to capture in a model."

Rather than abandon the project, Cunha and Morrison teamed up to see if they could fix the model. Specifically, they asked: If the model wasn't replicating real-world data, could they use that data to fashion a better model?

Enter the embedded discrepancy operator. You can picture this tool, which Morrison first developed to study the physics of combustion, as a sort of spy that sits within the guts of a model. When researchers feed data to the tool, it sees and responds to the information, then rewrites the model's underlying equations to better match reality.

"Sometimes, we don't know the correct equations to use in a model," Cunha said. "The idea behind this tool is to add a correction to our equations."

The method worked. After letting their operator do its thing, Morrison and Cunha discovered that they had nearly eliminated the gap between the model's results and public health records.

Being honest

The team isn't stopping at Zika. Morrison and Cunha are already working to deploy their same strategy to try to improve models of the coronavirus pandemic.

Morrison doubts that any disease model will ever be 100% accurate. But, she said, these tools are still invaluable for informing public health decisions--especially if modelers are upfront about what their results can or can't tell you about a disease.

"This epidemic has revealed how difficult it is to model a real system," Morrison said. "But I hope that people don't take that to mean that we shouldn't trust our scientists."