Skip to content

Posts from the ‘Micrometeorology’ Category

How To Estimate the Impact of Radiation Sources in the Environment

What impact does direct solar radiation have on the overall radiation balance? Dr. Colin Campbell, WSU Environmental Biophysics professor and METER scientist, shows you how to do the calculations in our latest chalk talk.


Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk.

Have you ever been outside on a hot day walking in the full sun and then stepped into the shade? The relief is almost immediate. And I was thinking about that a lot when I was looking at this graph here, the estimated crop water loss on one of my experiments.

So this is an ET zero, meaning a reference ET. But since I was working grass, that was actually the estimated water loss from this grass crop. And what I noticed was that the shape of this curve kind of went up, and then went down. And it kind of matched right here, the solstice, the summer solstice. And in my mind, I thought, you know, what impact is direct solar radiation have on the overall radiation balance? Well, we can quickly just jump down and look at the equation that talks about how we might estimate the evapotranspiration from a crop. I’m not going to be able to have time here to get into what each of these variables mean.

But as you see, solar absorbed radiation, R abs is a strong component of that overall calculation. Now, when we talk about absorbed radiation, we need to understand that it’s not just all direct sunlight. In fact, if you assumed that, you’d be off in the weeds quite a bit, because it contains components of both longwave radiation, which is radiation that’s coming from your terrestrial surroundings, and shortwave radiation, that which is coming from predominantly the sun.

So let’s talk about that for a minute. With absorbed radiation, we have shortwave radiation. This is radiation that’s less than four micrometers. And we have longwave radiation. This is not surprisingly, from wavelengths greater than four micrometers. Now, this shortwave radiation, this comes from the sun longwave radiation comes from other sources, like trees, the sky, ground, just other objects that are around the temperature that we expect in the natural environment. Now, the truth of the matter is to get R abs, we need to combine both of these things into a single number. And it actually gets even more complex than that. So bear with us as we go on to the next equation.

R abs is a function of both shortwave radiation and long wave radiation. And when we calculate our radiation balance to get absorbed radiation, we have to actually take all of this into account. Now, you might be wondering, what are the other pieces in this equation, we’re going to spend a little time going over that. So you might understand how we can get from all of these numbers, all of these potential sources of radiation to a final number of R abs.

This portion of the equation here is shortwave radiation. And we’re going to talk about the variables in that equation. The first one we see is alpha s. It’s a number between zero and one. It signifies the percentage of shortwave radiation that the object can absorb. The other parameters in the equation include some F’s and some S’s. The F’s we call view factors, we’ll discuss view factors in more detail in another chalk talk. But suffice it to say that these essentially are parameters to estimate the amount of radiation that our object can see in its surroundings.

S stands for shortwave radiation. And this comes from several different sources. They include p: this is radiation that’s coming directly from the sun. That’s the one I mentioned earlier, that we feel if we’re standing in the direct sun, versus if we walk into the shade. But there are a couple of others. One is diffuse. This is the radiation that’s scattered as light comes into our atmosphere and it’s scattered by the atmosphere.

Finally, there’s R. This is reflected radiation, radiation that when it comes in, hits a surface, it reflects off that surface and comes and impinges on our object. Think about snow. If you’ve ever been skiing or out on the snow, you know, on a sunny day, you’re getting a lot of radiation that’s being reflected back. This portion of the equation over here is our longwave portion. Similar to our shortwave, it contains many of the same symbols, but they’re a little bit different.

The alpha L is the absorbed radiation. Now in the long wave that also goes from zero to one. The F is our view factor again, but now the view factor of longwave radiation, and L stands for that longwave radiation. This time, the subscripts A, that stands for atmosphere, and G stands for ground. If we put together all components in this equation, we’ll be able to solve for absorbed radiation. But that’s going to take a little bit of work. First, we need to understand the absorptivity of our surface both in the shortwave and the longwave.

The shortwave typically is calculated just from tables from looking out on the internet. For example, if I wanted to look at the absorptivity of a maple leaf, that’s typically around 50%. But it’s something that’s probably been calculated in literature. For our longwave radiation, almost all objects absorb long wave radiation at about 97 to 98% of the possible total.

So it’s pretty easy to estimate these absorptivities for objects that are fairly common. Calculating solar radiation and long wave radiation take a little bit more time. And especially understanding the view factors or how much of a particular surface our object sees, is going to take a whole chalk talk on its own. We’re going to leave this discussion here and leave for next time an opportunity to talk about how to calculate our shortwave radiation, or long wave radiation, and then get to the complicated discussion of view factors.

For more content like this, head over to our YouTube channel, or go to Thanks for watching METER chalk talks.

See METER environmental sensors → 

How to calculate the angle of the sun

Listen to Dr. Colin Campbell, WSU environmental biophysics professor, as he discusses how to calculate the angle of the sun, or solar zenith angle.


Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk. A couple of years ago, I was heading out into the backcountry and we wanted to figure out what kind of gear we should take along. A friend suggested we should just check the wind chill factor. But when I looked into it, we found out that it doesn’t even consider solar radiation in that calculation. Our exchange of energy in the environment is highly dependent on radiation, particularly solar radiation. And today, we’re going to talk a little bit more about that. Now the first thing to know about solar radiation is where the sun is in the sky. In fact, our absorbed radiation really depends on it. Interestingly, it’s one of the few things in life you can really count on.

With a few equations, we can figure out where the sun is in the sky at any time of the day. And I’m going to take you through some of these equations, one of the things I want you to know first is, they’re a little complicated, so don’t get stressed. In fact, if you just want to stop the video at a certain point. And check out these equations for a moment and write them down. That’s just fine. Now let’s just jump into it.

So here on my screen, I’m showing a graph of where the sun might be, at any point in a day if you were standing on the equator. Now in the middle, I’m going to draw this blue line across there, that is at the equinox. Now at the two solstices the sun might be here tracking across the sky, or here. And of course, this diagram is really showing kind of a fisheye picture of where that sun might be. There are two ways to describe where the sun is. One is a zenith angle. The zenith angle has a symbol, we call psi. In fact, the angle to the Earth’s surface from the perpendicular or normal, so this would be that zenith angle. Now there’s another angle we might be interested in, it’s called the Azmuth angle. But for our purposes of today, I just want to focus on this zenith angle because it’s the most important as we consider the radiation impact in an object that we’re interested in.

So to calculate the zenith angle, we’re going to go down and discuss the equation where this right here is zenith angle. And this here is the equation that we use to calculate that. Now you recognize the sines and cosines. And there’s just a couple other things in here. Of course, we’ve got t, which is time. And then a few other variables, phi. This is the latitude. Delta, this we call the solar declination, and finally, t zero, this is solar noon. Now before we get too crazy and worried about this equation, all we have to do is put in a few things into here, and we’ll be able to calculate that. So the first thing we need to know is the time of day.

Then we need to know the day of year. Now we actually call this a special name. This is called a Julian day. And it starts counting from January 1. The other things we need to know is of course, latitude, and longitude. And I’ll get to why in just a moment. The first parameter we’re going to try to find is called the solar declination. The solar declination equation looks pretty crazy. And anytime you see an equation like this in a book or something, the first assumption you should make is this is an empirical equation. As I look out on the internet and study other materials, I find that these equations actually are fairly common out there. And this isn’t exactly the way you see it in every piece of literature. But let me talk you through it here.

Really, there’s only one thing we need to know. It is the Julian day and we can go on the internet and calculate these a lot of programs just have those hard coded in like Excel. And all we need to do is just put that Julian day in for each of these values-here into here, and then we can eventually calculate the delta value. And then we can go put it back in this equation. So as long as we know the declination here, this is just the latitude. Let’s say my latitude is about 47 degrees. We just put that right here. All we need to know now is this t zero or solar noon. So what did we do for that?

Well, solar noon is calculated like this: t zero is equal to 12. That’s solar noon, and then we change it for wherever we are with respect to entered Meridian. And we call that the LC longitudinal correction, and then we also subtract off this equation of time t. We can start with the equation of time here. That’s this equation right here. And that’s not very small. In fact, not only is it not small, but it has a whole bunch of f’s in it. You can see f, here, this two times f, this is three times f, this is four times f. And now in the cosine or sines, then we have cosines here. So what is that?

Well, f is another one of these little bit long equations it is two point, or sorry, 279.575 plus 0.98565 times the Julian day. Now, if you get that, you just plug it back in here. And you can calculate your equation of time. And this is a number much smaller than one that you can plug in to this equation right here. Now, what about the longitudinal correction?

Well, the longitudinal correction Lc, that’s pretty straightforward. It’s essentially for every degree east of this of the standard meridian, you add 115. So for example, where I live, I’m at one 117.2 degrees, longitude, our standard meridian 120 degrees. And so the difference is, we’re east of that 2.8 degrees, and therefore the longitudinal correction, LC is just 2.8 over 15, or equal to 0.19h. So essentially, what I do is take that right there, and plug it in up here for the longitudinal correction. So essentially, we take 12, and we subtract off the longitudinal correction, and then with our equation of time, we get this value and eventually have t zero.

So what does all this mean? What does it sum up to? Well, there’s a lot of numbers in here. But if we go back to our initial equation, all we’re going to need to do now is simply this. We have our solar noon, we plug our time in. And then we use our solar declination here that we calculated on the first part of this discussion, our latitude here, and then suddenly, we’re able to calculate the Zenith Angle. And I’m going to try to link to a little calculation spreadsheet I did in Excel onto the sheet or onto the this video and then you can go ahead and look at that, how it’s done, and do your own calculations. For more content like this, check out our YouTube channel or head over to Thanks for watching a METER Chalk Talk.

Learn more:

Watch our Weather Monitoring Master class→

See METER environmental sensors

Are floods and droughts really unpredictable?

In our latest podcast episode, Kevin Hyde, manager of the Montana Mesonet, discusses his views on predicting and mitigating the effects of flood and drought.

Montana’s large geographical area makes mesonet equipment maintenance a challenge.

He also shares how to build a robust weather network with high-quality data on a small budget, why setups should include other measurements such as soil moisture and NDVI, and the genius way he handles maintenance over such a large geographical area.

Listen now—>


Kevin Hyde is the manager of the Montana Mesonet. Learn more about the Montana Mesonet project on their website. 

Montana Mesonet website


Follow us:


Our scientists have decades of experience helping researchers and growers measure the soil-plant-atmosphere continuum. 


The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.

Chalk talk: How to model air temperature variation with height

In his latest chalk talk, Dr. Colin Campbell, environmental scientist at METER Group, teaches how to model vertical variation in temperature and how to estimate sensible heat flux.

Video transcript

Hello, everyone. My name is Dr. Colin Campbell, and I’m a senior research scientist here at METER Group. For today’s chalk talk, we’ll be talking about modeling vertical variation in temperature. In Figure 1, I’ve put together a graph that shows the maximum and minimum temperature with height and depth in the soil at some snapshot in time at a particular place. 

Figure 1. Maximum and minimum temperature with height and depth at a snapshot in time, in a particular place

It’s interesting to note that the change in temperature with depth in the soil is much faster than the change in temperature with height, whether we’re talking about a maximum or minimum. And the reason is that even though air is a good insulator, it also mixes really well. And that mixing is caused by eddies. And there’s a little more to that story. It depends specifically on surface heating by the sun through radiation and the cover type, whether it’s plants, rocks, boulders, straight soil, snow, or wind.

Equation 1

If we were going to model that, we would start by writing an equation (Equation 1) where a temperature at sun height, Z, above the surface (see variables noted in Figure 1), is equal to an aerodynamic surface temperature, T0, minus the sensible heat flux, divided by 0.4 times rho, CP, which is the volume specific heat of the air, times a variable called u*, which is the friction velocity. We multiply all that by the logarithm of z, the height above the surface minus d, which is the zero plane displacement, divided by z h, which is a roughness parameter. You might notice up here in the list of variables, that the zero plane displacement is 0.6 times H. H is the canopy height in meters. The rough roughness parameter can be estimated as 0.02 times the canopy height or times H. Now we have an equation that will help us model temperature with height. 

However, often we don’t know things like H, our sensible heat flux, and u*, our friction velocity. One of the things that we notice about this equation is that it’s set up somewhat like a linear equation. As you know, an example of a linear equation is something like Equation 2.

Equation 2

Figure 1 isn’t written quite that way, but if we look closely at the example below (Equation 3), this value could be our b, and this value our m, and this value could be our x. And if we do that, we actually can get some use out of graphing temperature with height. 

Equation 3

So we went out one day and measured this with a METER Group set of environmental sensors set up at certain heights above the surface. Here we placed sensors at 0.2 m, 0.4 m, 0.8 m, and 1.6 m above the ground. 

Table 1

To visualize this, in Figure 2 we graphed height on the y axis and temperature on the x axis, similar to the graph in Figure 1.

Figure 2. Graph showing the relationship between height and temperature

We know from Equation 1 that the axes for temperature and height should be switched because temperature is the dependent variable, and height is the independent variable. So if we switch axes it would look like the graph in Figure 3.

Figure 3. Graph showing the relationship between height and temperature where temperature is the dependent variable and height is the independent variable.

Figure 3 is graphed with the independent variable on the x axis and height on the y axis. If we fit this curve with today’s calculators, it would be fairly easy to get a curve that would fit that. But since it’s a linear equation, we can take the temperature data from Table 1 and the In ((Z-d)/ZH) data from Table 1 and graph them together.

Figure 4. Relationship between temperature and In Z-d/zh

Figure 4 is a graph that shows what happens when we do that. Notice that, just like we suggested, it creates a linear equation (Equation 4). 

Equation 4

We learned in Figure 1 the B value was equal to t0 (our aerodynamic surface temperature). Since we know our surface temperature is 34.5 degrees, we can estimate what the temperature is down here at the surface, even though we only measured down 0.2 m. 

We also know from Equation 4 that our M value is equal to -2.01. And if we look at Equation 1, our slope value is below.

Equation 5 (the slope value from Equation 1)

So we can write

Equation 6

How to estimate sensible heat flux

Now, if we were interested in the sensible heat flux, which we often are, we can simply rearrange this equation to be

Equation 7

And in Figure 1, I forgot to give you this value, but for an air temperature of 20 degrees celsius, 

Equation 8

And then finally, a typical unit for friction velocity, which should be measured in the field over the specific canopy you are in, is about 0.2 meters per second.

Equation 9

So if we did this calculation, we would learned that there’s about 193 watts per meter squared of sensible heat flux coming off that surface. 

Equation 10

So if we can measure temperature at a few heights, we can estimate what the heat flux is coming off the surface assuming we know something about our canopy. Learn more about measuring and modeling environmental parameters at If you have any questions feel free to email Dr. Campbell at [email protected].

Learn more

Read about weather station best practices—>

Discover the ATMOS 41 All-In-One Weather Station—>

Download the Researcher’s complete guide to soil moisture—>

Episode 9: Pioneers of Environmental Measurement

What was the life of a scientist like before modern measurement techniques? In our latest podcast, Campbell Scientific’s Ed Swiatek and METER’s Dr. Gaylon Campbell discuss their association with three pioneers of environmental measurement.

Learn what it was like to practice science on the cutting edge. Discover the creative lengths they went to and what crazy things they cobbled together to get the measurements they needed.

Listen now—>

Ranching, Wildfires, & Global Sustainability

Check out our latest podcast, where Dr. Richard Gill discusses his global research projects including climate change on the Wasatch Plateau, ranch sustainability in Colorado, reef studies in Samoa, and wildfires in the Mojave Desert.

Landscape in Samoa

He focuses on the connection between the ecology of a place and the communities of people that inhabit it, and how scientists can protect socially and ecologically vulnerable populations by collaborating equally with them. Unless they’re sharks. He found out they’re typically not open to collaboration.

Listen now—>

Learn more about Dr. Gill

Richard Gill, PhD, is an ecologist and department chair in biology at Brigham Young University.

Links to learn more about Dr. Richard Gill:

Richard’s biography

Richard’s ResearchGate


Follow us:

Improve Your Plant Study: 3 Types of Environmental Data You May Be Missing

What data are you missing?

The environment plays a large role in any plant study. Ensuring you’re capturing weather and other environmental parameters in the best way allows you to draw better conclusions. To accurately assess plant stress tolerance, you must first characterize all environmental stressors. And you can’t do that if you’re only looking at above-ground weather data.

For example, drought studies are notoriously difficult to replicate and quantify. Knowing what kind of soil moisture data to capture can help you quantify drought, allowing you to accurately compare data from different years and sites.

Get better, more accurate conclusions

It’s important for your environmental data to accurately represent the environment of your site. That means not only capturing the right parameters but choosing the right tools to capture them. In this 30-minute webinar, application expert Holly Lane discusses how to improve your current data and what data you may not be collecting that will optimize and improve the quality of your plant study. Find out:

  • How to know if you’re asking the right questions
  • Are you using the right atmospheric measurements? And are you measuring weather in the right location?
  • Which type of soil moisture data is right for the goals of your research or variety trial
  • How to improve your drought study, why precipitation data is not enough, and why you don’t need to be a soil scientist to leverage soil data
  • How to use soil water potential
  • How accurate your equipment should be for good estimates
  • Key concepts to keep in mind when designing a plant study in the field
  • What ancillary data you should be collecting to achieve your goals

Register now—>


Holly Lane has a BS in agricultural biotechnology from Washington State University and an MS in plant breeding from Texas A&M, where she focused on phenomics work in maize. She has a broad range of experience with both fundamental and applied research in agriculture and worked in both the public and private sectors on sustainability and science advocacy projects. Through the tri-societies, she advocated for agricultural research funding in DC. Currently, Holly is an application expert and inside sales consultant with METER Environment.

Evapotranspiration: Pitfalls to Avoid and Why It’s Easier Than You Think

Mistakes that kill your estimates

Measuring evapotranspiration (ET) to understand water loss from a native or a managed ecosystem is easier than it looks, but you have to know what you’re doing.

Learn causes and implications of uncertainty

If you can’t spend the time or money on a full eddy-covariance system, you’ll have to be satisfied with making some assumptions using equations such as Penman-Monteith.

Like any model, the accuracy of the output depends on the quality of the inputs, but do you know what measurements are critical for success? Plus, as your instrumentation gets more inaccurate, the errors get larger. If you’re not careful, you can end up with no idea what’s happening to the water in your system.

Get the right number every time

You don’t have to be a meteorologist or need incredibly expensive equipment to measure ET effectively. In this 30-minute webinar, Campbell Scientific application scientist Dr. Dirk Baker and METER research scientist Dr. Colin Campbell team up to explain:

  • The fundamentals of energy balance modeling to get ET
  • Assumptions that can simplify sensor requirements
  • What you must measure to get adequate ET estimates
  • Assumptions and common pitfalls
  • How accurate your equipment should be for good estimates
  • Causes and implications of uncertainty



Dr. Dirk V. Baker has been with Campbell Scientific since 2011 and is an Application Research Scientist in the Environmental Group. Areas of interest include ecology, agriculture, and meteorology—among others. He has a bachelor’s degree in wildlife biology and a doctorate in weed science, both from Colorado State University. Dirk’s graduate and postdoctoral research centered around measuring and modeling wind-driven plant dispersal.

Dr. Colin Campbell has been a research scientist at METER for 20 years following his Ph.D. at Texas A&M University in Soil Physics. He is currently serving as Vice President of METER Environment. He is also adjunct faculty with the Dept. of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics, a class he took over from his father, Gaylon, nearly 20 years ago. Dr. Campbell’s early research focused on field-scale measurements of CO2 and water vapor flux but has shifted toward moisture and heat flow instrumentation for the soil-plant-atmosphere continuum.

Learn more

Download the “Complete guide to irrigation management”—>

Smart orchard aims to install thousands of sensors for actionable insights

When big data is a problem

Orchard growers today live in an exciting time where environmental data are becoming inexpensive and abundant. But going from a data-poor to a data-rich environment has its challenges. Big data can be so overwhelming that growers struggle with how to turn that data into actionable insight.

In March, began piloting a smart orchard project in collaboration with researchers from Washington State University & Oregon State University at Chiawana Orchards in Washington state.

One grower on the Washington Tree Fruit Research Commission recently commented that he uses no less than 19 data apps for making decisions. Steve Mantle, founder of, says, “It’s just overwhelming to a grower to consolidate all of this data together. We need to figure out how to help them with actual insights that impact either their yield quality / quantity—and just as importantly—their costs: particularly on labor, chemical/nutrients, and irrigation.”  That’s why in 2020, Mantle and his team approached the Tree Fruit Research Commission’s technology committee to see if they could bring their capabilities, ingesting data from many different data silos and sensor providers into one place, with the goal of providing actionable insights for growers in the apple orchard space. Thus, the idea of a “smart orchard” was born.

Turning big data into a solution

In March, began piloting a smart orchard project in collaboration with researchers from Washington State University & Oregon State University at Chiawana Orchards in Washington state. Their goal was to “sensorize” an orchard from multiple hardware providers, bringing together growers, data, and researchers to create a sustainable, “smart” orchard with insights that impacted a grower’s bottom line. To do this they combined data from on-farm and off-farm, online and offline sources including satellites, drones, weather providers, telemetry from IoT devices such as soil moisture probes and leaf wetness sensors, and more.” Mantle adds, “We’re trying to see how the sensors at different price points and from different vendors compare against each other in terms of accuracy. But the biggest goal is to get more granularity around and prove the value in canopy, soil, and weather measurements. Then we tie that in with yield, quality, and profit.”

Installing sensors so that comparisons are valid

The smart orchard consists of 100 rows of Gala apple trees spaced out over two 20-acre blocks. A number of different sensor/instrumentation providers, including METER Group, have their sensors deployed at this smart orchard measuring parameters such as weather, irrigation, soil water and nutrients, chemicals, disease, pests, crop health, labor, and drone/satellite imagery. All these data are aggregated and organized on a regular basis to try and enable growers to better understand weather and climate change to make precise, informed decisions and better manage their water usage, labor, equipment, and chemical usage.

Smart Orchard team member and researcher, Harmony Liu, says one challenge they face is making sure the comparisons are valid. “We are careful to install the same sensor types at the same heights so we are making “apple-to-apple” comparisons.”

Liu says in addition to sensing, they collect soil samples every week throughout the season and send them out to two different labs for nutrient testing so they can look at how that data compares with the soil nutrient sensors. They sample at five different locations at three different depths to match the sensors. She adds, “We have the dendrometer, soil nutrient data, soil moisture data, and canopy data all being collected within the same zone. It’s part of our intent to show this data all connecting with each other.” The team also measures irrigation line pressure with a sensor as opposed to using an irrigation switch. Liu says, “We want to know what the pressure signature is as everything turns on and activates so we can understand what that signature looks like and start to identify when there are abnormalities in how the irrigation system fills.” Additionally, they’re using METER NDVI and PRI sensors as well as a pyranometer for ground truthing the drone imagery that they’re doing at a 7 centimeters per pixel resolution.

The goal is understanding in-canopy weather and how to work with institutions on adapting models for disease, pests, and ultimately informing spray management.

Data cleanup is time-consuming

Liu says getting the smart orchard up and running was not without its challenges. “The first challenge was gaining access to some of the data from grower owned instruments because those instruments are not all grouped together.” Liu says that challenge made data cleanup time consuming, but they worked their way through it. She adds, “Overall, having this density of data is difficult because it’s a lot to wade through. But at the same time, it’s been really helpful. Data has been reliable coming in across the board.”

In-farm vs. outside-farm measurements

Liu says one thing they are interested in is accurately measuring temperature and humidity within the orchard because these parameters are critical for apple disease modeling. She says, “When people are modeling disease, they take the inputs from weather forecasts into the disease model for risk calculations. But there are some differences in environmental conditions inside vs. outside the orchard where evapotranspiration will cause temperatures in the canopy to be cooler compared to outside-farm temperatures while the vapor pressure is higher. So that’s one thing we use METER group instruments for. We have outside-orchard,  above-orchard, and in-canopy ATMOS 41 weather stations and ATMOS 14 temperature and relative humidity sensors. We use these to compare the temperature and relative humidity difference. By using an instrument from the same provider, we eliminate the systematic bias vs. if we were to compare temp and RH from different providers. We also set up a vertical profile by installing sensors on the same pole at different heights and could see how the temperature and humidity changed across height for that location.”

Register for the smart orchard project live webinar with this Thursday, Jan.14th at 4pm PST.

Future smart orchard goals

Mantle says their most important goal is understanding in-canopy weather and how they can work with WSU and other institutions on adapting models for disease, pests, and ultimately informing spray management. Liu adds, “We also want to understand data comparison and unification. We want to bring together soil moisture measurements like volumetric water content and data from the METER TEROS 21 matric potential sensor. What we found is that, although they’re looking at soil moisture from different perspectives, unifying the two measurements will be critical for people working on irrigation scheduling.” The team also plans on working with WSU professors to create an evapotranspiration map that blends together some of the sensor telemetry and the view from a drone.

See the webinar

Want to learn more? METER soil physicist, Dr. Colin Campbell and Washington State University soil scientist Dr. Dave Brown discuss the smart orchard project in a METER Group webinar.

View more METER crops webinars—>

Learn more

Download the “Complete guide to irrigation management”—>

What’s causing the home run spike in America’s favorite pastime?

In baseball, the aerodynamic properties of the ball are critical. Pitchers rely on the ball to have a certain amount of lift so it can move in the air to the left or right based on how they throw a curveball.

Did something change on the surface of the ball that allowed more home runs?

The ball can also speed up or slow down based on the amount of drag, or how much the air is resisting the ball’s movement. Because of this, federations such as Major League Baseball want to ensure the ball doesn’t change from year to year. In the past couple of years, a spike in home runs prompted the Major League Baseball federation to contract Washington State University sports science laboratory to help them better understand the aerodynamics of the ball. They want to know: did something change on the surface of the ball that allowed more home runs to be hit? 

Tiny changes cause big problems

Even the slightest change in the surface of the ball, such as the seam height or the seam width, can have a large effect on the carry distance of the ball. Jeff Kensrud, assistant director of the WSU Sport Science Lab says, “These balls sometimes hit the wall 350 feet in the outfield, and if that ball would have travelled five more feet, it would have gone over the fence. That’s why we’re trying to quantify the aerodynamic properties of the ball so professional organizations can tell how far the balls are going to travel in the air when they’re hit.”

Kensrud says they have two experiments set up in the lab. One is an environmentally controlled chamber where they do all the bat and ball testing. He says, “We have to maintain 72 ℉ and 50% relative humidity 24 hours, 7 days a week because balls and bats change with temperature and humidity.” The second experiment is an aerodynamic test lab where temperature and humidity are more challenging to control. They need to understand what the microclimate is because weather variables like temperature and barometric pressure are constantly changing, which can affect their calculations. 

The ATMOS 14 sensor measures temperature, RH, barometric pressure and vapor pressure

To monitor these parameters, the team uses ATMOS 14 temp/barometric pressure/vapor pressure/and relative humidity sensors and a ZL6 data logger to get near-real-time feedback of what’s going on in their Aero Testing Lab. Kensrud says, “Essentially, we have three sensors hooked up to the ZL6 logger at the beginning, the middle, and the end of our aerodynamic test tunnel. Every half hour, we’ll check ZENTRA Cloud software and put that data directly into our computer so we have the most up-to-date weather information to ensure we’re running our aerodynamic experiment correctly. The tunnel is 60 ft long, so we need to know if the temperature is the same throughout our testing station. A slight change in barometric pressure could alter our calculated coefficient of drag. Sometimes in a matter of a few hours a storm can roll in. And if we are checking that sensor regularly, then we can see drift in our coefficient of drag and we know it had nothing to do with the ball. It’s just the barometric pressure in the city of Pullman changed.” 

How tests are run

To test the aerodynamic properties of the balls, the team projects balls into still air at up to 100 miles an hour. They then determine what the coefficient of drag and lift is, or how the balls behave in the airflow. And the team also tests bats.

Researchers measure bat performance using real baseballs

Kensrud says, “For 15 years, we’ve been the exclusive certification center for testing and certifying baseball bats for USA baseball, USA softball, NCAA baseball and most of the amateur baseball world. If you want to certify your equipment for play, it’s going to come through our lab.”  For bat testing, researchers measure bat performance using real baseballs. The lab uses pneumatic air cannons that accelerate the ball from at rest up to about 140 miles an hour. The balls are released from the cannon and then impact the bat. Kensrud says, “It’s as close as we can get in a laboratory to a real bat/ball collision on the field.”

Sensors help maintain proper test conditions

Kensrud says using the ATMOS 14 confirmed their hypothesis that there was sometimes a change in temperature over 60 feet in their concrete building where tests are performed. He says, “These sensors are constantly telling us if we have a temperature gradient and how severe it is. If you start out at 75 degrees at the beginning of the tunnel, but by the time you get to the end you’re at 82 degrees, that could be a problem. Especially when you assume your temperature is constant. These sensors help properly monitor the temperature throughout the space and also give a very accurate barometric pressure reading in near-real time. That’s critical for what we’re doing because in this area we can get big storms coming in and out that can affect our measurements. If we have a change in barometric pressure or a temperature gradient of more than two degrees from the beginning to the end, we shut testing down to allow the space to acclimate. The data enables us to set up a go/no go approach to aerodynamic testing.”

Why ball aerodynamics matter

Kensrud says that this research is important to keep the ball constant.

Ball aerodynamics affect both pitching and hitting.

He says, “Some federations need to know when or if the ball has changed. Ball aerodynamics affect both pitching and hitting. The more precise our instruments are in monitoring, the more accurate the results are that we give to these Federations so they can make informed policy decisions on what to do with the ball, or how to keep the ball in spec for years to come.”

What’s the future of aerodynamic sports testing?

Kensrud says he’d love to continue testing for federations like Major League Baseball, but it would be fun to grow and expand into measuring the aerodynamics of other sports balls. He adds, “It’s a bit of a stretch, but it wouldn’t be impossible to think that one day we could be making some high-speed, in-flight, aerodynamic measurements for say, the aerospace industry. But that’s not a contract. That’s me putting my ‘future cap’ on.”

You can learn more about WSU sports science lab testing here.

Learn more about monitoring weather variables. Read: “Weather Station Comparison: Which Weather Monitoring System is Right for You?