Lauren Lavin:
Welcome to this episode of our podcast, Plugged In to Public Health. My name is Lauren and I will be your host for today in the first of a two-part series featuring Dr. Jun Wang, an accomplished scientist and professor with deep expertise in atmospheric sciences.

Dr. Wang is the James E. Ashton professor in the College of Engineering, Chair of the Department of Chemical and Biochemical Engineering, and Assistant Director of the Iowa Technology Institute at the University of Iowa. His pioneering research combines satellite remote sensing and chemistry transport models to investigate critical topics such as air quality, wildfires, and intricate interactions between aerosols, clouds, and the land atmosphere system.

Dr. Wang has published over 210 research articles, holds an H index of 65, and has contributed to 10 satellite missions, reflecting his remarkable influence in this field. He also holds an impressive list of accolades, including NASA’s new Investigative Program Award, the AGU Joanne Simpson Medal, and the Iowa Board of Regents Award for Faculty Excellence.

In this conversation, Dr. Wang shares his journey from a rural community in eastern China to becoming a leading researcher in atmospheric sciences. He delves into the early inspirations that sparked his interest in weather and climate, his educational and research experiences, and his perspectives on the collaborative interdisciplinary nature of atmospheric research. Let’s dive into this insightful conversation with Dr. Jun Wang and get plugged in to public health.

Thank you for being on the podcast, Dr. Wang. I really appreciate you taking time out of your day to talk with us. My first question for you is a little bit about your career inspirations and give our listeners a little background on you. What sparked your interest in atmospheric sciences, and how did you develop this career path and end up at Iowa?

Jun Wang:

Yeah, thanks for the opportunity, Lauren.

So I grew up in the eastern part of China in a pretty remote area that basically is a very rural community. So from early on in my childhood, I appreciate the importance of weather to the day-to-day lives in farming. And then the change of the climate year-by-year can have huge consequences on the agricultural yield. The one part of the things that my parents were both farmers, so I appreciate all the importance of the changing of the weather and the climate. And I [inaudible 00:02:37] find it very fascinating because each day the weather are different. And sometimes you see the rain in one side of the river, but you don’t see the rain in the other side of the river. That’s kind of pretty cool, I thought.

And another part of that is that in my hometown in Nantong, when I was a kid, we also flied kite a lot. Those are not the kites that normally you get from Walmart. Those are huge ones. These are all kind of the… You made by your own with hand. And they were very huge, and they can actually fly very high, and make different kind of sounds. And then if you wanted to fly that kite very high in the sky, you have to navigate. You are flying that kite in the air by knowing the wind directions, by wind speed around the kite in the right angle in order for them to fly up high and stay high. So I found that also fascinating when I was a kid. All these two elements, why is really day-to-day life going to affect our weather? Another one is a toy that you play with your childhood friends all day long. And that weather is really interesting, is a critical thing that I can think of to study weather.

So when I graduated from high school, I went to a university to study my bachelor degree in atmospheric science. And I supposed to be a weatherman to work with the National Weather Service to provide our seven weather forecast for the neighborhood. After I graduated with a bachelor degree, I did pretty well in my academics and I thought that maybe I should actually give a shot to just go to U.S. to study more about how the weather really work instead of using a tool. The way the prediction generally use a model or to predict the weather.

So rather than using the model output to say tomorrow how the weather look like, I want to study how that model works. That requires a advanced degree. So I went to the Chinese Academy of Sciences Institute of Atmospheric Physics in Beijing. That’s one of the top academic institutes for studying atmosphere science. And they have, at that time, the best computational facility there to do the prediction of the weather. So [inaudible 00:04:57] exactly the prediction of the weather is basically using computers, and then together with the data you collect around the world, to do a prediction of the weather.

And I learned a lot there with a master degree in Beijing. And I actually did some forecasts for some [inaudible 00:05:15] events and all that. At that time was in 1990s. There are many of my classmates at that time want to study more and get advanced degree in the United States. And so I kind of joined a crowd of the graduate students with master degrees come to the U.S. to do their PhD in atmospheric science. So I end up in the atmospheric science at University of Alabama Huntsville. The University of Alabama Huntsville, it’s located in Huntsville, which is also co-located with the NASA Marshall Space Flight Center.

And they’re well known for designing Saturn V rocket to launch, in Apollo era, launched the [inaudible 00:05:58] huge rocket and they’re able to launch the human to the moon and such. So they have a very huge facility in research in [inaudible 00:06:06] the aerospace and the atmospheric science and many other things around the satellite remote sensing. So I went there. I spent very good almost six years there to get my PhD there. So this is where you really don’t know how the life leads you. I [inaudible 00:06:22] study weather, but you want to study weather, predict the weather, you need to know what the current weather condition look like.

The air moves. You know the air is coming from the north to the south are cool air coming. You know the next couple of days, the weather will be cooler. But in order to know that cooler air is up north, move to your location, you need to have data to observe what we have right now. And then you can only put that many of the weather stations over land. You cannot put that weather station anywhere and there are many developing countries that cannot afford for it. It turns out the satellite remote sensing, the satellites from space, the weather satellites has advantage point. It apply global monitoring capability of the air temperature, the cloud movement, especially many of the atmospheric conditions, the weather conditions, cloud movement over the ocean, in our planning, 70% is covered by the ocean. So you need to know how those things are happening over the ocean in order to predict what’s happening over land. So satellite almost become a key tour there.

So I learned a great deal and [inaudible 00:07:37] become one of my expertise in satellite remote sensing of atmosphere. And then afterwards I did my PhD and then I had an opportunity to go to Harvard University to study basically air quality. Because at that time, one of the [inaudible 00:07:54] to study fires at that time. That’s stand still almost 20 years ago, but at that time most of the fires occur in the developing countries because the farmers in these countries using fire as a tool to clear up the forest. For land example, the Amazon.

Lauren Lavin:
Yeah.

Jun Wang:
Clear up the forest for farming. There are also [inaudible 00:08:19] proper residuals after harvest, so you recycle all the nutrients back to the soil. However, that also cause a lot of environmental problems because we burn things. Burn the crops, burn the biomass. Most of them, it goes to carbon dioxide, become a greenhouse gas and effect, right? Did have greenhouse gases. On the others and they also generate a large amount of smoke particles which lead to the very poor air quality. So I want to study atmospheric chemistry, how the atmospheric composition is changing. So I went to Harvard to a postdoc there. I finished the postdoc for two years there and I had an opportunity to become a faculty member at the University of Nebraska-Lincoln.

Over there, I joined many students. And in study, some of them are now actually working in the National Weather Service. I have one of my former students in [inaudible 00:09:09] City National Weather Service. I have another student in Omaha worked for the Air Force [inaudible 00:09:16] Agency, another student work for the Naval Research Lab. Turns out that the weather prediction is widely needed. You have many things we do. Many things we do. And then so I spent nine years there, and then went here in University of Iowa, had an opportunity to do big data because they are looking for a faculty member to do big data in atmospheric position and that’s a good fit for me. So I decide to apply here and I got the job. That’s where I’m here since then and I have been here for eight years. So that’s where I kind of end up here. It’s kind a long story.

Lauren Lavin:
That was a great background. You gave us a lot of info about your discipline as well as some background about you. I also love hearing why people have picked their chosen field and starting just when you were a little kid. You can just see how that has always been part of your story. I have a couple of follow up questions.

Jun Wang:
Sure.

Lauren Lavin:
Number one, so I didn’t know that, well, I guess I probably could have guessed, that models are used to predict the weather. So my question is how accurate are these models?

Jun Wang:
That’s a very good question. So this model basically is the realization of all the physical laws, chemical reactions, and the best knowledge we have about what we know regarding the air and the land and how the earth, as a system, works.

So for example, we definitely have equations similar to F = MA, which is Newton’s law, also have ideal gas law, giving a [inaudible 00:10:50] pressure, you can able to correct the volume of the air, things like that.

We also have as conservative equations because the air mass are not change much. We also have the equation to describe the phase change of the water from water vapor to ice to liquid and how they are exchanged. Or have all those equations put in the model.

So essentially, this model is a realization of a set of many partial derivative equations as a functional time. In order to solve those equations, you have to know two things. One is the initial condition or boundary condition. You have to know our current stage, current condition, how the weather look like in terms of temperature, air movement, the clouds, water vapor, you name it, including the soil. Today we had rain, guess what? The temperature, this [inaudible 00:11:43] will be much cooler then you don’t have rain because the soil will be evaporated. So it’s a system. So you need that kind of information to do the prediction.

Another part of the story is that this part of [inaudible 00:11:53] equation, unfortunately don’t have analytical solution. That is not like DY over DX equal = A, then Y = X + B. That’s simple because you have this equation is not… There is no analytical solution for it. Therefore, you have to use computers and to design what we call numerical scheme to serve them empirically, more or less, approximation form. As a result of that, your prediction has several errors.
One error is that you don’t really know perfectly current condition what the weather look like. For example, you don’t know one or two miles above the surface what the temperature look like exactly. You don’t know right after the rain how much soil moisture exactly we have [inaudible 00:12:41] in our city. We don’t know that. So we can only estimate that. And lots of source of errors get passed to the models.

Another source of error is what we call parameterization because ideal gas flow, for example, is able to simulate the air temperature, air molecular movement, all those things. But our computer is not big enough to track each air molecular. So we have to do the parameterization. Basically we divide the globe into different grid boxes, different chunks. So we call it grid box spatial resolution. In the U.S., our day-to-day weather prediction maybe can get you up a three kilometers, maybe one half mile spatial resolution. But within that spatial resolution, we cannot resolve, for example, things like irrigation. Maybe irrigating alone in our neighborhood. But that water adding the soil, we’re not be restricting our predictions. So there are things that is because of that approximation is simplified, so that’s a source of error as well.

So those errors are, together, lead to the weather [inaudible 00:13:46] errors. So I would say that numerical weather prediction starts during the World War II at that time, because for any military operations you need a weather intelligence. So you need to know if there are dust storms, there are sun storms, things like that. So people have been trying to do the weather prediction since using computers. I would say the prediction accuracy has hugely improved in the last 50 years. Now I think the weather prediction is actually pretty accurate. Very accurate. We’re talking about 90% in-

Lauren Lavin:
Wow.

Jun Wang:
Yeah, in part, it’s because of the satellites. Satellites have global coverage that tell you where are the areas [inaudible 00:14:27] the temperature is. We’re putting a lot of satellite data into the predictions. But still, the more harder problem is that adding to other system.

If you have a fire, then you can get a larger amount of smoke particles in the air. And that layer of smoke particles in the daytime will reflect a lot of sunlight back to space. So you will have a shadow on the ground, your temperature will be cooler. Traditionally, it was not considered in weather prediction. Now today we try to use that to integrate into the weather prediction, make it more accurate. It’s more like you have a curve here. In the beginning, it’s relatively easy to improve the accuracy without solid data, but slowly that curve getting steeper and steeper to get accuracy increased by 1% or 2%, which is how to spend a lot of effort to get that more closer to the perfection. So anyway, that’s kind of how we’re doing weather prediction today.

Lauren Lavin:
I didn’t realize how many factors went into it. And you’re right, there’s so many things that there’s no way that you can account for, like people watering their lawns.

Jun Wang:
Right.

Lauren Lavin:
Kind of like what you said, going back to when you were a child, rain would be on one half of the river but not the other. There’s all these little localized systems that there’s just no way you could factor all of that.

And then my other follow, because you were talking about these weather satellites, how many of those are there circling the planet?

Jun Wang:
Oh, those are on the hundreds.

Lauren Lavin:
Oh, wow!

Jun Wang:
Yeah. Weather community is very different from rest of the community. The weather and science community are very collaborative because we know air moves. Sometimes can moves. Look at the hurricanes and stuff like that. It can moves 100 miles, 80 miles, 50 miles per hour. So we have to collaborate in order to do the weather prediction. From early on, there were weather meteorologist, people who do the weather studies, always very collaborative. We share the data all the time. Because otherwise you cannot do a prediction. The satellite, these are civilian sides of the satellites. For the United States, we have constantly two geostationary satellites. That one look at the East Coast, another one with the West Coast, and they all cover the central U.S.aAnd provide the data almost every 5 to 10 minutes.

Lauren Lavin:
Wow.

Jun Wang:
On the cloud movement, on the lightning, on the hurricane, on the convective systems, on the crop growths, they are constantly giving that. And then NASA launch a lot of satellites, each country has their own civilian satellite for weather. So on order of hundreds.

Lauren Lavin:
Your work spans various disciplines. It’s obviously collaborative. And I know that it includes maybe some public health components as well as some agricultural components, and so how does the collaboration or all of these interdisciplinary people help to enhance your research on weather?

Jun Wang:
That’s a very good question. So I give you one example. For example, on my master degree, I primarily started at atmospheric dynamics. Basically how the air moves, wind speed, and all that prediction. But then later on, I want to study cloud formation. And we want to study cloud formation, we want to know how the water vapor were condensed into liquid water, how the liquid water become ice, and those processes we call a micro physical process. And those processes requires chemistry. The knowledge about chemistry, how let’s say a smoke particle will provide a service for water vapor to condense. Because if in a very clean atmosphere, there is no particles. Therefore, that water vapor itself coalesces and become water droplets. It’s very difficult. But if you have some little third water service for water vapor to condense, they’re much easier to condense. They will form liquid water and form rain.

So this process get into the micro chemistry side of things, some particles more easier for water vapor to condense than others. Like sulfuric acid is more easy for water to condense because they have affinity with respect to water. Dust is harder, but dust is good for water vapor to directly become ice. So those things need chemistry component to it, so I study atmospheric chemistry.

And then another part of the story is, for example, the crops, right? Look at the winter time. Winter time, there are no crops. Therefore, when the sunlight come in on the surface, most of the sun will be reflected back to the atmosphere in the winter time. But if you have the summertime, you have green canopy, photosynthesis will happen. This solar radiation will be used by plant rather than to increase the temperature, it was used to become [inaudible 00:19:20] for crops to do photosynthesis [inaudible 00:19:22] food.

So in that case, you need to work with the folks who studied ecology, who study the plants to know how you can estimate how much solar radiation is used by plants for their own growth rather than increase air temperature. So I give you three example like this. So we… And then we deal with satellite remote sensing. We also deal with people who are making satellites and [inaudible 00:19:50] I work with them a lot. They are from all the disciplines in engineering field. From system engineering to electrical engineering to communication to aerospace engineering to optics design, so you start to expand your boundaries to know more and more [inaudible 00:20:09] also find you learn from different people in different fields how the things [inaudible 00:20:13], yeah.

Lauren Lavin:
I agree. That is fun. And I think it’s so interesting how all these fields can come together and weather, in particular, impacts so much of our lives. So it’s not surprising to me that it’s a really interdisciplinary field.

Now we’re going to talk a little bit about the lab that you run, which is the Atmospheric and Environmental Research Lab. Can you explain some of the core research areas for the AER lab at the University of Iowa?

Jun Wang:
Yeah, sure. One side of the things that we do… Actually, I would say four component now as our lab need to grow. The first component is really the satellite remote sensing of the fires, of air source, air pollution, that’s one component.

Another component is a model prediction. So how do we assimilate? We call assimilate. Basically use the satellite data. [inaudible 00:21:07] satellite data into the models to better predict the weather, better predict the air quality, better predict the climate change. That’s the second part.

The third part is try to bring these two together to help NASA, to help different agencies to plan their future satellite missions. Satellite missions cost a lot of money. And once up there, it’s very difficult to fix or change if they’re in the air. So how do [inaudible 00:21:39] satellite that serve your purpose with limited amount of the money? It’s a very challenging question.

In the past, we just get the point and we design this way, and then we launched the satellite to space. But if later, we find, “Oh, some of the design actually useless,” some of the design could be have done better. Then we just [inaudible 00:21:56] that knowledge for the next design. But with the current market where the SpaceX launch, the launch of satellite getting cheaper and cheaper, there’s a new technology for small satellite launch and all that. There is a desire to load more and more satellites in smaller weights, in more smarter manner. We have a niche here is try to help design the satellite with computing. So we’re saying if we design the satellite this way, what kind of data you’re going to get? By combining our modeling capability and the satellite simulation capability, we can produce the synthetic data, we call it synthetic data, that’s saying, “If the satellite designed this way and flying this way, these are data you’re going to get.”

So in a computer, in this way, we’re simulating. We call the observation system simulation experiment. We call it Aussie. We basically say, “Well, if you design this way, what can you have?”

By doing so, the design is more objective, but it’s also cheaper. So we can provide a menu of options for these funding agencies or for anyone [inaudible 00:23:01] satellite to say, “Well, here’s the limit of your budget. You do this way versus that way, here’s the data you get.” So they decide what’s the path forward. So that’s the third component.

The last component is really try to do more in additional research. Biotech [inaudible 00:23:17] University of Iowa is a very comprehensive university. We have people like you from popular health, we have people from pharmaceutical, from business, from law, art, science, engineering. But the ambient atmosphere affects all of us. 10 minutes, if you don’t have oxygen in 10 minutes, we need to have [inaudible 00:23:37] air.

So air quality affects human’s lives. And then in Iowa, the weather is dynamic. You see weather effects. So we want to do more of those things to collaborate with people from different disciplines to see how the weather intelligence, like our weather [inaudible 00:23:53] information help to improve our sustainability of our planet, to improve people’s day-to-day planning, their quality of life. So we do a lot of the interdisciplinary work to help to do the precision agriculture, air quality prediction, or do renewable energy. The wind prediction is needed because in Iowa, the renewable energy is big. We are number one, number two in the nation in how many wind energy production. So you want to know more how the wind look like, right? And the things like that. So there are a lot of [inaudible 00:24:22] work in surrounding areas. Even including how the weather affect your mood. For example, we find on the Facebook, these are in older study, we find in the Facebook in the winter time or the summer time people are generally hot in our summer time than in winter on the Facebook because of weather effect. The four components [inaudible 00:24:41]-

Lauren Lavin:
Okay, so this is where I’m going to cut the episode for this week. Thank you so much for joining us in the first part of this conversation with Dr. Wang. His journey from a rural upbringing in China to his groundbreaking work in atmospheric sciences at the University of Iowa really showcases his deep commitment to advancing our understanding of climate, air quality, and environmental sustainability. Dr. Wang’s insights into the power of satellite technology and data modeling reveal just how interconnected our atmosphere is with our health, agriculture, and energy sectors here in the US and how collaborative research can really be and how it can lead to innovations that improve all of our worlds.

In the next episode, we’ll continue our discussion with Dr. Wang, exploring more about his lab’s latest projects, the future of remote sensing, and his thoughts on the next big challenges in atmospheric science, so make sure you tune back next week for that part of it. We look forward to having you back for part two and we’ll dig even deeper into his work. Until then, thank you for listening and stay tuned to next week.

This episode was hosted and written by Lauren Lavin and edited and produced by Lauren Lavin. You can learn more about the University of Iowa College of Public Health on Facebook. Our podcast is available on Spotify, Apple Podcasts, and SoundCloud. If you enjoyed this episode and would like to help support the podcast, please share it with your colleagues, friends, or anyone interested in public health, and make sure to hit that subscribe button.

Have a suggestion for our team? You can reach us at cph-gradambassador@uiowa.edu. This episode is brought to you by the University of Iowa College of Public Health. Until next week, stay curious, stay healthy, and take care.