Understand your choices

Choosing the right weather station can be confusing. Hundreds of options exist for weather monitoring ranging from $200,000+ aviation-grade observation systems to $25,000 WMO-grade mesonet stations with redundant rain gauges and multi-height wind and temperature observations, all the way to $300 hobbyist-level stations. How do you know which system is right for you? And what is the sweet spot for price vs. maintenance vs. accuracy for your unique application?

Find your sweet spot

In this 20-minute webinar, METER research scientist, Dr. Doug Cobos explores the research weather station price vs. utility continuum. Find out:

• Why you need weather data as an ancillary measurement, even if your primary measurement needs are in the soil or plant community
• Why you should consider data quality vs. maintenance and measurement parameter combinations in your cost analysis
• 3-season vs. 4-season performance
• Which situations require low-, medium-, or high-grade solutions, and how high should you go?
• Pros and cons of different solutions
• How does the ATMOS 41 weather station compare to other methods?
• Where is the sweet spot for performance divided by price in your application?

Next steps

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Questions?

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Presenter

Dr. Cobos is a Research Scientist and the Director of Research and Development at METER.  He also holds an adjunct appointment in the Department of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics.  Doug’s Masters Degree from Texas A&M and Ph.D. from the University of Minnesota focused on field-scale fluxes of CO₂ and mercury, respectively.  Doug was hired at METER to be the Lead Engineer in charge of designing the Thermal and Electrical Conductivity Probe (TECP) that flew to Mars aboard NASA’s 2008 Phoenix Scout Lander.  His current research is centered on instrumentation development for soil and plant sciences.

Transcript

BRAD NEWBOLD 0:07
Hello, everyone, and welcome to Weather Monitoring 101: Which Weather Station is Right for You? Today’s presentation will be about 20 minutes followed by about 10 minutes of Q&A with our presenter Doug Cobos, whom I’ll introduce in just a moment. But before we start, we’ve got a couple of housekeeping items. First, we want this webinar to be interactive. So we encourage you to submit any and all questions in the Questions pane. And we’ll be keeping track of these for the Q&A session toward the end. Second, if you want us to go back or repeat something you missed, don’t worry, we are recording this, and we’ll be sending around a link to this recording via email within the next three to five business days. All right. With all of that out of the way, let’s get started. Today we’ll hear from Dr. Doug Cobos, who will discuss how to know which weather monitoring system is right for you and what is the sweet spot for price versus maintenance versus accuracy for your unique application. Doug Cobos is a research scientist and the director of the Environment Group here at METER. He also holds an adjunct appointment in the Department of Crop and Soil Sciences at Washington State University, where he co teaches environmental biophysics. Doug holds a master’s degree from Texas A&M and PhD from the University of Minnesota focused on field scale fluxes of CO2 and Mercury, respectively. Doug was originally hired at METER to be the lead engineer in charge of designing the Thermal and Electrical Conductivity Probe that flew to Mars aboard NASA’s 2008 Phoenix Scout lander. His current research is centered on instrumentation development for soil plant and atmospheric sciences. And without further ado, I’ll hand it over to Doug to get us started.

DOUG COBOS 1:46
All right, thanks, Brad. Thank you for the nice introduction. And welcome, everyone. Thanks for joining in today. I imagine that most of you are working from home like we are, and so go ahead and get your slippers adjusted, get comfortable, and let’s get going. So today I’d like to talk to you about weather monitoring, and the you know, wide variety of options that you have available to you in terms of weather stations. So I’m not going to touch on the reasons that we might want to monitor weather, monitor environmental conditions, because if you’re tuning into this virtual seminar, then you’re very familiar with those things already and those options there. Those reasons that we might want to make above ground measurements are almost limitless. And sometimes it feels like the options that we have available to us to make those measurements are also limitless, almost an infinite number of combinations that you could have. And so that can be a little bit daunting when trying to figure out which weather monitoring solution is right for your particular research or your particular routine monitoring application. But what I want to talk about today is, first thing I want to introduce is this graph that I that I made up that has performance of the weather station or the weather monitoring solution on the y axis and has price on the x axis. And I’ve plotted just a linear plot here. And we all know that this is not 100% true, but just for the sake of argument, let’s say that you get what you pay for typically, okay, that that the price versus performance continuum is kind of a straight line. But what we want to talk about today is not necessarily the price versus performance strictly, but this transverse axis that I’ve plotted here, and this is our value axis. And so what this is, is if you get a better price to performance ratio, then that’s a higher value for the measurement needs that you have. And so what is it that’s going to affect this value proposition? Well the price of all these instruments is set, so it’s really the y axis, the performance of a particular instrument for your particular need, that changes this value proposition. And so there are a lot of different factors, many, many different factors that affect the relative performance of a weather monitoring solution or instrument. And that is really defined by the needs of your measurement—your particular measurement needs. So, for example, you may have a need to make measurements in a remote location where you can’t access the site routinely. And so this site has to be— the instrumentation has to be extremely robust, okay. So if something breaks, well, maybe this is an application where if a sensor breaks, then a flash flood isn’t detected, and people’s lives are in jeopardy. So the robustness of that weather monitoring solution is what’s going to drive the relative performance, okay. Maybe you’re a climatologist, and you are monitoring air temperature, and you need to monitor this for climate change and get a continuous accurate record of air temperature for several decades. And so in that case, the accuracy of that instrument and the stability of that instrument is going to be the driving factor that affects the performance relative to your measurement need, and therefore, that value proposition. Maybe you’re running a huge network of weather monitoring instruments, and the cost of making a field trip to do routine maintenance and the cost of installation is significant, and many times actually dwarfs the cost of purchasing the equipment in the first place. And so if this is where you sit with your measurement needs, then maybe the maintenance requirements of the instrument are what drive the performance. Often, well, those of us who work in the research realm often need specialized measurements, okay, so just air temperature and humidity and rainfall may not help us satisfy the research needs. And so the measurement suite, the specialized measurements that we need, are what drive the performance of the system relative to our particular needs. Remote data acquisition is almost ubiquitous now, but getting real time data is very important for especially some monitoring situations where, again, human life may be in jeopardy. Some systems have four season capabilities, some systems have three season capabilities, meaning that some systems are heated and can function and give accurate results in high latitude wintertime, others don’t. Maybe that’s a really important thing for you, maybe you’re studying, you know, wintertime precipitation, and you need a heated rain gauge that can still capture the snow and make those measurements. Maybe you’re just doing an agricultural study, and the fourth season isn’t important to you, because plants aren’t growing. So these are the kinds of things that you need to think about when you’re deciding on a measurement solution for yourself. Power requirements are also important. If you’re doing remote research or remote monitoring, and there’s a battery powered system, then that’s going to be an important factor that drives the performance and the value proposition to you. So these are just a few examples of the different factors that can affect the performance in the value proposition.

DOUG COBOS 7:35
So if we get back to this graph, just to kind of demonstrate how this works. Perhaps you are running aviation weather systems at airports, okay. And so in that case, this highlighted weather station is an AWOS, an ASOS station that has specialized measurements that are necessary for aviation. You can’t get away from those. You have to have those. And those are what drives the performance of this system up, okay. Maybe this particular system is one that has a lot of instruments that need routine maintenance. And so you can’t scale your network because of the routine maintenance requirements. And so the value of this, the performance and the value, has now fallen off. Maybe this particular system uses a rudimentary rain gauge that just measures the sound generated by water droplets hitting a drum basically, and it’s kind of a qualitative measurement. And maybe you’re trying to do a water balance study and you need precise rain gauge measurements. And so this would drive the performance and value down. Maybe this instrument has very low power requirements and is able to be powered by double A batteries indefinitely. And so maybe if you have a large network of systems that are that are remote, and you don’t want to have to go and change batteries often, well, this could be a good option for you. Maybe this system down here has an anemometer, a cup anemometer that tends to break off and freeze up, and you need something with low maintenance, and so maybe this one’s not a good choice. Maybe you’re a homeowner, and the WiFi capability in a little console that sits on your desk and tells you what the weather is outside of this system is exactly what you need. And so that drives the performance, the relative performance, up and therefore the value up. And so now what we’ve done is taken these various offerings, some of them, off of our straight line and looked at our particular measurement needs, figured out where the performance is relative to that line, and which ones are more valuable to us. And so now we can make better educated decisions on which system we might use for our research or for our monitoring.

DOUG COBOS 9:56
So, before we get too far along this line I want to do just a little bit of definition of the various classes of weather stations that that we might encounter. And so kind of a specialized class, but the high end is the aviation weather and really the thing that differentiates these systems and drives their performance, and therefore their value, are the specialized observations. And so you can see on the far left, there’s a visibility and present weather sensor that will tell you what the distance that a pilot can reasonably see would be pretty important for aviation. One in the middle as a ceilometer, that tells cloud height. The one on the far right is an even cooler instrument that tells you about freezing rain, that if you have any ice buildup. And so these are the kinds of specialized measurements that wouldn’t be found on most weather monitoring solutions. But they do drive the performance of these aviation weather systems. These systems also have specialized communications with VHF transmission and redundant phone systems, have to be extremely robust because human health and safety do depend on these. They need four-season performance unless they’re in the tropics. And the accuracy of these is typically pretty important because most of these data are piped into the the climatological record. And so these are really the— if you look at the performance versus price continuum down here on the right, these occupy the upper right corner where the performance is very high in many ways, and the price is also very high. These may be $200,000 plus systems.

DOUG COBOS 11:33
The next class, general class, that I want to talk about is the WMO or World Meteorological Organization compliant class. And so often your national weather networks in various countries and some of the Mesonets which are medium scale networks, adhere to the WMO recommendations and guidelines. And one of the important things about the WMO stations is that these need a 10 meter tower. You can see in the picture that there’s a 10 meter tower, that some of the measurements are made at 10 meters. Some of the measurements are made lower down in the atmospheric profile at two or three meters. And there are also significant maintenance requirements necessary for these stations to adhere to the WMO, to the letter of their recommendations. You have to maintain these pretty often, these need four-seasoned capabilities, they also need really high accuracy. So these are often the stations that feed into our climatological record and tell us things like, Hey, we are actually getting warmer. So these are really high end systems and very nice systems. You can see that on that price performance continuum, they sit kind of up in the upper right as well. It may cost you $20,000 to $50,000 to install one of these systems, and there are significant maintenance requirements. And so the yearly cost of operating these systems is pretty high. So these are really nice tier one stations. But really their cost is prohibitive for dense spatial networks. So you can put these on a grid, and these are the stations often that our weather predictions come from, that the data feed into the models. But you can’t really afford to put in really dense spatial networks of WMO class stations.

DOUG COBOS 13:21
So I struggled a little bit with a way to quantify almost everything else. But certainly, those of us who work in the research realm often need custom systems that have a measurement suite that is tailor made for the research that we’re trying to do. And you will see these also in various weather networks, where these custom stations will have a measurement suite really that satisfies the needs of whoever the constituents are. And so, on top of your normal weather parameters, you might have surface temperature from a infrared thermometer, you might have NDVI, you may need redundant rain gauges in case one has a problem with baffling around him, you may need net radiation for various surface energy balance studies. The upper right corner here shows a dual eddy covariance system measuring isotopic ratios in— I mean, you can dream up almost an infinite number of measurements that you can make. And that’s why if you look at the price versus performance continuum, these are scattered all over the place. So you can come up with a custom system and integrate a bunch of individual sensors into a data acquisition backbone and make pretty much any measurement that you want. And so these are what you will often see for the non mesonet and non national weather networks. So you’ll see a lot of custom applications out there.

DOUG COBOS 14:56
In the last couple of decades, we have seen a proliferation of all in one weather stations. And so instead of piecing together weather stations that are a bunch of custom sensors that integrate into a data acquisition backbone, what a lot of manufacturers have done is integrate the various weather measurements into a small package all in one station. And these are pretty ubiquitous, there are lots of these available out there from a lot of different manufacturers. And so you have a lot of different options on your measurements. And you have a lot of different options on price point. So these typically will, an all in one station may cost you somewhere between $1,000 and $5,000 depending on the measurements that you choose. And if you want a three season or a four season instrument and some of those other considerations. These all in ones are really, really nice from an installation and often a maintenance standpoint, very much less complexity than than a custom station or a WMO station. And so they’re pretty good option for dense networks. What you’re seeing recently is the WMO class stations, you know, make up the backbone, but then the gaps, the spatial gaps, between those WMO class stations are filled in by the all in one stations to give a denser network and much richer information. And really the drawback on these in the minds of many decision makers is that these cannot strictly follow the WMO recommendations. So you’re making all your measurements at one height, so you can’t have the air temperature and wind speed measurements at 10 meters, and then have the other measurements at two meters. So that is a drawback of these stations. So these have their niche, just like the WMO stations have their niche.

DOUG COBOS 16:58
The last class that I’d like to talk about are what I call the hobbyist stations. And so these are typically built for homeowners, and you might see some of these in commercial buildings and things like that, but these are stations that are not particularly robust and not really well suited for research or long term monitoring. The really the nice part of these is the data acquisition and communication will beam the information into some of these consoles. And it’s pretty nice to have, you know, localized weather measurements at your house or at your place of business. And so there is a niche for these as well. But I won’t talk about these much more because mostly what you guys care about, I imagine, is either research or or long term monitoring. Of course, if you’re tuning into this webinar, you may be a weather junkie, kind of like I am, and you may want one of these at your house. So you can find quite a number of these if you go and do an Amazon search.

DOUG COBOS 17:56
So for the rest of the webinar, I would like to dive a little bit deeper into the value proposition and talk about some case studies where a particular solution was chosen and talk about the reasons that that particular solution was chosen. And so the first case study that I want to talk about is Penman Monteith, reference evapotranspiration. So if you are tuning in here, you’re probably familiar with, well, you may be familiar with Penman Monteith, which is a mechanistically based equation that quantifies the amount of evapotranspiration or water loss from either a grass surface or an alfalfa surface. So, it takes the weather variables and says, If you had a grass surface, this is how much a well watered grass surface or alfalfa surface, this is how much water vapor you would lose to the atmosphere. And so, this is a measurement that is commonly made in irrigated agriculture, generally in high dollar irrigated agriculture like vineyards and fruit trees, but also in other center pivot applications for agriculture because the farmers, the growers need to know the water balance. They need to know how much water has been lost from the system, how much water has been gained by the system so they can replenish any of that water that has been lost, the net loss, with irrigation water. And so for this particular measurement need, these growers need these localized measurements at a lot of different locations. And this is an example where growers especially don’t want a very complex system. They want something that’s easy to set up, easy to install, and has very low maintenance. Also, it’s pretty attractive to have a remote system with just a little bit of battery usage. And so if you can, you might notice that the data logger here has just a small solar panel that will run this weather station indefinitely. And really the key driver of choosing this particular weather station for the Penman Monteith or FAO 56, a reference evapotranspiration, is that the growers need both solar radiation and precipitation. So they need to know the amount of precipitation that’s coming in and replenishing the water in the soil. And they also need that solar radiation measurement for the Penman Montieth or the FAO 56 reference evapotranspiration measurements. And so this is a a case where, interestingly, a lot of the all in one stations don’t have precipitation and solar radiation. And so that’s kind of a unique feature of the ATMOS 41 is that it does have solar radiation, and it has precipitation. And so the measurement suite here is really a good choice for this kind of monitoring in the agricultural setting. And so you can see, there’s a graph here on the left, this is this ZENTRA Cloud software that automatically makes those reference evapotranspiration measurements on a daily and accumulative basis, and allows you to add the crop coefficients to convert from reference evapotranspiration into true evapotranspiration, and so this is a really nice turnkey system for the farmers.

DOUG COBOS 21:20
The second case study that I’d like to talk about is completely different. Okay, so the one that we just talked about is, you know, ease of installation and maintenance and some specialized measurements. While this is a different application where our friends at Campbell Scientific were involved with a project to engineer and develop and build some weather stations that have been deployed on Mount Everest. And the highest one of those weather stations is the highest elevation weather measurement station that is active in the world right now. And so, if you guys can imagine the conditions on Mount Everest, the robustness of this station is the key driver that drives up the performance for this particular measurement need, and therefore the value proposition. So the station is ultrarobust, I would call it a four season station, but it’s really not, it’s really a one season station, right. It’s just measuring wintertime conditions as we would think about them. It’s got redundant measurements. You can see the specialized anemometers up here that have special coatings to shed ice and snow, with redundancy in case one freezes up. You can tell there’s redundancy in some of the other measurements as well. But really, this is not a project that was driven by price considerations. This is something, I mean, the cost of trying to go up and maintain the system dwarfs the cost of the system by probably orders of magnitude. So really neat project, I would encourage you guys to go to Campbell Scientific site and read about this pretty fun project. But this is a case where if you look down at the bottom right graph, the robustness of this system was the performance driver and therefore the value proposition driver.

DOUG COBOS 23:12
Third case study that I’d like to talk about is near and dear to my heart because this is run by Washington State University where I spend a little bit of time teaching. And this is the Washington State AgWeatherNet. And so if you look at the picture up here, on the top, you’ll see a whole bunch of green dots and these have numbers in them. The numbers are just the temperature in degrees Fahrenheit, at the time that I grabbed this off the internet. But each of these green dots is a AgWeatherNet, tier one weather station. And you can see that these are concentrated primarily in the agricultural regions of Washington state. There are a few out in Idaho, a few down in Oregon, but mostly these are in the fruit basket of United States. So these are all your apple orchards and the high dollar crops that really, along with California, feed much of the United States. So AgWeatherNet’s calling is to install some observation stations. And you can see down at the bottom left, here’s one of their tier one stations. And the measurement suite that they measure here is tailor made for the growers in the particular region. So what AgWeatherNet does is ingest data from these stations that they put up and output a whole bunch of model parameters like disease models, pest models, frost prediction, frost monitoring, and these are things that are very much value added for the producers in the region, which actually pay for the system. So one of the things that’s been really interesting with the AgWeatherNet is that although this looks like a dense spatial network, these are, you know, these stations are many kilometers apart. And what they’re finding out is, hey, I have a really accurate tier one station that’s sitting right down here in this valley, okay. But the measurements that I make might be two degrees C different from those at this orchard at the top of the hill. And so if I continuously monitor temperature and humidity and come with a prediction for, I don’t know, some plant disease, some fungal disease, well, that prediction here is going to be way way different from the reality up here. And so what AgWeatherNet is doing now is allowing individual growers to purchase and install these tier two systems that you can see here, that is just an all in one weather station that doesn’t have quite the accuracy specs of the tier one stations, but the lack of accuracy at the point scale is almost inconsequential compared to the spatial difference in the weather parameters as you move away from the tier one sites. And so these tier two stations are creating hyper-local observations, and then AgWeatherNet is using artificial intelligence, along with those hyper-local observations to do hyperlocal predictions for the growers who put these stations in. And they’re having a lot of success with this, to be able to predict mold, to be able to predict pest outbreaks, to be able to predict frost events at a particular grower’s location. And so this is an instance where the measurement suite of the tier one stations drives their value, but the installation and maintenance and in some ways, the low cost of the tier two stations are driving the value for the particular growers. And so this is kind of an interesting two tiered approach that I think is pretty smart way to go.

DOUG COBOS 27:12
Okay, the final case study that I want to talk about is weather in Africa. Interestingly, there are— well, outside of the country of South Africa, there are almost no weather observations in the whole continent of Sub-Saharan Africa. So a group that we have partnered with called TAHMO, or Trans African Hydro Meteorological Observatory has the calling, what they’re trying to do is put 20,000 weather stations in Africa. It’s pretty difficult to predict weather if you can’t even observe the weather. And so this has a lot of negative repercussions for weather prediction, for crop insurance, and really is a big negative for the African farmer, and one of the reasons that it’s been very difficult to get efficient farming practices adopted over there. So with TAHMO, they had some some really important considerations that drive the performance and the value of the stations that they wanted to install. First of all, it has to be a simple installation. Their ground crews are not particularly skilled, but the biggest driver is the low maintenance. In many regions in Africa, it is extremely difficult to make field visits to instrumentation. Okay, there’s civil unrest, there’s political instability, there are militias roaming around. And so routine maintenance trips to fields outside of maybe once a year are very, very difficult and very expensive. And so we partnered with TAHMO to make a all in one weather station that was as robust as we could possibly make it, with no moving parts to freeze up. And so TAHMO has now installed upwards of 500 of these in Africa and is, at this point, the largest operational weather network on the African continent. And the low maintenance part of this is actually bearing out quite well as TAHMO has about a 95% uptime. And interestingly, the Aviation Weather systems in Sub Saharan Africa generally run about 66, 67% uptime, which is a little bit scary if you’re flying into some of these airports, but the uptime on the all in one stations are actually beating out the AWOW and ASOS stations in Africa and so we’re pretty proud of that. The marketing guys allowed me to put this in here— actually asked me to put this in here, so I had to go ahead and include it.

DOUG COBOS 29:51
But hopefully, those case studies get you guys thinking about the right, what I think is the right thought process when you’re selecting a weather station. So really, you define the performance of the station in the value, and it’s your measurement needs that define that performance. So what you need to think about when you’re choosing a weather station is, what are the most important factors? Does this have to be super robust? Does this have to be hyper accurate? Is this something that is going to be sitting outside, you know, at the field plots down the road that my technician can visit and maintain, you know, once a week? Or is this something that I’m only going to be able to visit once every two years, okay. What are the particular measurements I want? Does it have three season versus four season capabilities? What are the power requirements? Is something that I have to put out that needs to run indefinitely on some small batteries? And so if you think through these various factors that drive the performance relative to your measurement needs, then it becomes a lot easier to decide what’s important. And then you can go out and find the best value. And so if you guys have— you know, want to discuss this type of stuff, I’m always available. And I will freely admit that our sales team calls me the sales impedance team, because I often send people to other manufacturers if there’s a better fit from somebody else. So with that, I’d like to open it up for some questions. And thanks for joining in today.

BRAD NEWBOLD 31:32
Awesome. Thanks, Doug. So it looks like we still have some time for questions. Thanks, again, to all those who have submitted questions. And there’s still plenty of time to do that. We’ll try to get to as many as we can. If we do not get your question, we do have them recorded. And if Doug does not answer them right now during this webinar, he will answer them or somebody from our METER Environment team will be able to get back to you individually via email, phone or other ways. So yeah, feel free to submit any and all questions that you have. Let’s see, first off, Doug. Any advice for greenhouse measurement station selection?

DOUG COBOS 32:22
Yeah, greenhouses are an interesting environment to measure in. So some of the all in one stations are pretty well suited for that. And you can find, you know, a measurement suite that doesn’t include precipitation, so you’re not paying extra for that, and part of the challenge in the greenhouse, though, is artificial lighting. So you have to pay a little bit of attention to that. If you’re trying to, you know, measure PPFD or photosynthetically active radiation, then you probably need to pay a little bit of attention to your quantum sensor, because most of the greenhouses now are going to LED lighting which emit in discrete bands. And if your quantum sensor doesn’t measure in that band, then you’re going to get the wrong answer. But there are plenty of options if you’re just looking for temperature and humidity, and I mean, even wind is important sometimes. But yeah, there’s a lot, I would suggest one of the all in ones for that.

BRAD NEWBOLD 33:23
Great, this next question. And they’re stipulating that this is very project dependent. But is there a rule of thumb or something about what would be the most and least frequent data logging for conventional meteorological parameters for soil plant atmosphere interaction?

DOUG COBOS 33:39
Yeah, that’s a really great question. I mean, in one that’s a little bit complex, because most of the power expense of a lot of these stations is broadcasting the data back to the cloud. And for weather monitoring, and for near real time observation, I mean, a lot of those are 5 minutes to 15 minutes. But if you’re just doing, you know, monitoring for a field study in the soil plant atmosphere continuum, then I tend to err on the side of only collecting data every 30 minutes to each hour. So most of the stations, you can program up your logger, or most of the stations are pre programmed to give you maximums and minimums and gust wind speeds so you don’t have to over sample to try and collect those things. And so, I mean, I am not one who likes to wade through terabytes of data in trying to do the post processing, so I tend to err for most soil plant atmosphere studies on the side of collecting data and broadcasting data less frequently.

BRAD NEWBOLD 34:49
Awesome. What type of system would you recommend for highly granular monitoring such as hillside grape production?

DOUG COBOS 34:57
So you’re certainly not gonna be able to go with one of the WMO stations or a number of the WMO stations. And this is really where the all in one stations are tailor made. You can find all in one stations for, you know, in the 1000s, a couple of $1,000 range that will make really nice accurate measurements that you can, you know, put several of those even in a vineyard, depending on topography, and understand the spatial differences that are driving, you know, that are going to drive your irrigation decisions in that vineyard. So the all in ones are really nice for things like that.

BRAD NEWBOLD 35:37
How about, how do you test sensor performance and the need for sensor recalibration?

DOUG COBOS 35:42
Yeah, that’s a really great question, and it’s something that we do quite a lot. So typical way to do that is, well, not every research group or every group is going to have the budget to do this. But what you would do is buy, you know, the tier one sensors, right, so for instance, a tier one pyranometer, that’s, you know, got traceability back to Davos and and compare your solar radiation measurements against that. And you would buy really nice, well calibrated, probably platinum resistance thermometer with an aspirated radiation shield to measure air temperature, and then you would compare your air temperature measurements against that. And so if you make these studies over the long term, over years, you can quantify the drift in a particular sensor and then come up with some reasonable recommendations for recalibration. And we spent a lot of time doing that with our ATMOS 41 sensor. And so we have some, what I feel are pretty good recommendations. I mean, we try and quantify the drift that we’ve observed and come with recommendations for recalibration or refurbishment that makes sense.

BRAD NEWBOLD 36:49
You mentioned about dealing with maintenance costs and those. How would you include into that equation or calculation support from the various companies that are producing these stations?

DOUG COBOS 37:07
Yeah, that’s a good question. I mean, the maintenance costs are significant, obviously. Sending people out to maintain, you know, stations, especially in large networks is expensive. My feeling is that most of the manufacturers of this instrumentation are going to give good support. And if you have a problem, you’re going to be able to find the answer. Now, I want to not disparage anybody in particular. But if you go to Met Tech Expo and look at all the instrumentation that’s there, and you go to the American Meteorological Society meetings and look at all the new instrumentation that is springing up, there are a number of new companies that are coming with offerings that look very much like instrumentation from reputable companies with a long track record, but may not have the performance, so I would be a little bit careful with some of the— I don’t know what the right word is to say without being rude— some of the less tested companies, how about.

BRAD NEWBOLD 38:23
How important is metadata for the representativity of the data series and model calculations?

DOUG COBOS 38:29
Yeah, that’s a really great question and one that we’ve given a lot of thought to so. So understanding, you know, when your various sensors were produced, when they were calibrated, understanding the location and the heights, and having that metadata record is extremely important. So for the WMO stations, that’s a no brainer, I mean, all that stuff is called out, you have to have, you know, the supporting metadata. But even for research applications, you get into so many situations where, you know, you have a grad student who’s doing great work, and then he or she, you know, graduates and runs off to their next job. And all of a sudden, their lab notebook disappeared, and you’ve got a whole bunch of data coming in, but you don’t know what those data mean, because you don’t know where those data are coming from, and you’ve lost all the supporting information, all the metadata that really make those data meaningful, and your next grad student or postdoc tries to pick it up, and they’re lost. And so it’s one of the things that we have spent a lot of time on, and are spending a lot of time on, having those metadata available all the way from the sensor through the ZL6 data logger and into ZENTRA Cloud, and so you have those metadata in all of your records and all of your permanent records, and it’s something that we’re still building out, but the building blocks are there and most of the relevant metadata come through already. We just need to continue to build that out because that is really important.

DOUG COBOS 39:59
Okay. It looks like we’re getting to the end of our time. So we’ll go with one more question here. And you mentioned just in passing the ATMOS 41 as well as the importance of seasonality or at least having a good robust four season. What have we seen issue-wise with or performance-wise with ATMOS 41 during all four seasons or at least during winter, extreme winter conditions?

DOUG COBOS 40:27
Yeah, good question. So the ATMOS 41, our all in one weather station, is a three season instrument. It is not heated. So the main drawback is that you will get no precipitation measurements, your funnel will just fill with snow, and you won’t get any precipitation measurements during the frozen part of the year. There is also the possibility that snow and ice could gum up the sonic anemometer opening and attenuate wind speeds and you’ll also get a little bit less accuracy in the air temperature measurement. Now with that said, I mean even though we have some pretty harsh winters here in Washington state, you know, northern tier state in the US, I’ve been pleasantly surprised with the measurements, if you’re willing to forego the precipitation, which a good wintertime precipitation measurement is a pretty intensive process. You have to have a heated gauge. Typically these are weighing gauges that you put a little oil on top so they don’t— and antifreeze to make sure that it doesn’t freeze and that you don’t get evaporation and so it’s really power intensive and and pretty difficult to do right. So that’s one that the ATMOS 41 will definitely struggle with.

BRAD NEWBOLD 41:41
Alright, thanks. And again, if you do have any interest in any of METER’s products or services when it comes to weather monitoring, feel free to visit us at metergroup.com Also you can request a demo. We have support or Doug himself or someone along those lines will be able to walk you through using the ATMOS 41 along with our ZENTRA Cloud system. But that’s going to wrap it up for us today. Thanks again for joining us, wherever you are. We hope you enjoyed this discussion. Again thank you for all these great questions. We do have a lot of questions that we did not get to. Doug or someone else from our METER Environment team will be able to get in touch with you via email to answer your questions directly. Please also consider answering the short survey that will appear after this webinar is finished just to let us know what types of webinars you’d like to see in the future. And finally, look for a link to today’s presentation, along with the slides that will be available in your email in the next few days. And stay tuned for future METER webinars. Thanks again. Stay safe and have a great day.